NTIA Report TR-05-419 

Interference Potential of 

Ultrawideband Signals 

Part 1: Procedures to Characterize Ultrawideband 

Emissions and Measure Interference Susceptibility of 

C-band Satellite Digital Television Receivers 

Michael Cotton 

Robert Achatz 

Jeffery Wepman 

Brent Bedford 

U.S. DEPARTMENT OF COMMERCE 

Carlos M. Gutierrez, Secretary 

Michael D. Gallagher, Assistant Secretary 

for Communications and Information 

February 2005 

iii

DISCLAIMER 

Certain commercial equipment, instruments, or materials are identified in this report to specify 

the technical aspects of the reported results. In no case does such identification imply 

recommendation or endorsement by the National Telecommunications and Information 

Administration, nor does it imply that the material or equipment identified is necessarily the best 

available for the purpose.

v

CONTENTS 

Page 

1. INTRODUCTION ...................................................................................................................... 1 

1.1. Purpose and Approach of This Study ................................................................................................2

1.2. Organization of This Report ..............................................................................................................3

2. INTERFERENCE SIGNALS ..................................................................................................... 4 

2.1. Signal Description .............................................................................................................................4

2.1.1. Direct-Sequence Ultrawideband.................................................................................................4

2.1.2. Dithered-Pulse Ultrawideband ...................................................................................................6

2.1.3. Multi-Band OFDM Ultrawideband............................................................................................7

2.1.4. Gated Gaussian Noise ................................................................................................................9

2.2. Computer Simulation.......................................................................................................................10

2.2.1. Reduction of System Complexity.............................................................................................10

2.2.2. Reduction of Samples...............................................................................................................12

2.3. Implementation on the Vector Signal Generator .............................................................................13

3. INTERFERENCE CHARACTERIZATION MEASUREMENTS.......................................... 14 

3.1. Metrics and Data Needed to Compute Them ..................................................................................14

3.2. Description of Test and Measurement Equipment ..........................................................................14

3.2.1. Vector Signal Analyzer ............................................................................................................14

3.2.2. Spectrum Analyzer...................................................................................................................16

3.2.3. Digital Oscilloscope .................................................................................................................17

3.2.4. Power Meter .............................................................................................................................18

4. VICTIM RECEIVER................................................................................................................ 19 

4.1. Satellite Digital Television Description...........................................................................................19

4.1.1. MPEG-2 Encoder .....................................................................................................................19

4.1.2. Forward Error Correction and Modulation ..............................................................................19

4.1.3. Receiver....................................................................................................................................20

4.2. DTV Quality Metrics.......................................................................................................................21

4.3. Operational Scenario Chosen for DTV Interference Susceptibility Tests.......................................22

4.4. Satellite Signal Simulation ..............................................................................................................23

5. INTERFERENCE SUSCEPTIBILITY TESTS........................................................................ 24 

5.1. Methodology....................................................................................................................................24

5.2. Test System......................................................................................................................................24

5.3. Procedures .......................................................................................................................................26

5.3.1. Calibration................................................................................................................................26

5.3.2. DTV Quality Metric Acquisition .............................................................................................26

6. SUMMARY.............................................................................................................................. 27 

7. ACKNOWLEDGMENTS ........................................................................................................ 28 

8. REFERENCES ......................................................................................................................... 29 
vi

APPENDIX A: MATHEMATICAL SIGNAL DESCRIPTION.................................................. 31 

A.1. Direct-Sequence Ultrawideband.....................................................................................................31

A.2. Dithered-Pulse Ultrawideband........................................................................................................32

A.3. Multi-Band OFDM Ultrawideband ................................................................................................33

A.4. Gated Gaussian Noise.....................................................................................................................35

APPENDIX B: VECTOR SIGNAL GENERATOR OPERATION ............................................ 36 

B.1. Operation in the Dual AWG Mode.................................................................................................36

B.2. Techniques to Optimize RF Output Performance...........................................................................37

B.3. Waveform Conditioning .................................................................................................................39

B.4. Operating Procedures......................................................................................................................40

APPENDIX C: EFFECTS OF VSG LO FEED-THROUGH ON DTV INTERFERENCE 

SUSCEPTIBILITY TESTS .......................................................................................................... 41 

C.1. LO Feed-Through Inherent to DTV Systems .................................................................................41

C.2. Experiment......................................................................................................................................43

C.3. Results.............................................................................................................................................44

C.4. Effect of VSG LO Feed-Through on Carrier-Suppression Ratio....................................................46

C.5. Conclusion ......................................................................................................................................47

APPENDIX D: ESTIMATING AND GRAPHING THE AMPLITUDE PROBABILITY 

DISTRIBUTION OF COMPLEX-BASEBAND SIGNALS........................................................ 48 

D.1. Estimating the APD ........................................................................................................................48

D.2. Plotting the APD.............................................................................................................................49

D.3. Testing the Routines .......................................................................................................................50

D.4. Additional Comments.....................................................................................................................50

D.5. MATLAB Code..............................................................................................................................52

APPENDIX E: DYNAMIC RANGE OF THE VECTOR SIGNAL ANALYZER ..................... 54 

E.1. Dynamic Range Measurement ........................................................................................................54

E.2. Average Power Measurements of Fundamental Signals.................................................................54

E.3. APD Measurements of Fundamental Signals..................................................................................55

E.4. Conclusion.......................................................................................................................................55

APPENDIX F: TEST SYSTEM CHARACTERIZATION.......................................................... 61 

F.1. Characterization of the Signal Paths................................................................................................61

F.2. Dynamic Range of Interference Signal Path Cascaded with the VSA ............................................63

F.3. Intermodulation Products in Satellite Signal Generator..................................................................63

APPENDIX G: HARDWARE SPECIFICATION ....................................................................... 66 

G.1. Interference Generation Subsystem................................................................................................66

G.2. Calibration Subsystem/Test and Measurement Equipment ............................................................67

G.3. Satellite Simulation Subsystem ......................................................................................................68

G.4. Coupling Subsystem .......................................................................................................................70

G.5. Receiver Subsystem........................................................................................................................71

APPENDIX H: GLOSSARY........................................................................................................ 72 
INTERFERENCE POTENTIAL OF ULTRAWIDEBAND SIGNALS 

PART 1: PROCEDURES TO CHARACTERIZE ULTRAWIDEBAND EMISSIONS AND 

MEASURE INTERFERENCE SUSCEPTIBILITY OF C-BAND SATELLITE DIGITAL 

TELEVISION RECEIVERS 

Michael Cotton, Robert Achatz, Jeffery Wepman, and Brent Bedford1

In this study, we hypothesize that ultrawideband (UWB) interference potential 

can be quantified in terms of UWB signal characteristics. To test this hypothesis, 

a test system was designed and built to inject UWB signals with known 

characteristics into a C-band satellite digital television receiver and quantitatively 

measure interference susceptibility via signal quality metrics (e.g., segment error 

rate, pre-Viterbi bit error rate, and modulation error ratio) taken from various 

points in the receiver signal processing chain. UWB signals are characterized by 

the amplitude probability distribution and power spectral density. 

Characterization measurements done with a vector signal analyzer provide 

amplitude and phase information to enable extensive post-measurement 

capability. This report describes the test setup and procedures in detail. 

Subsequent reports will provide assessment of interference potential for gated 

Gaussian noise bursts (Part 2) and modern UWB systems (Part 3). 

Key words: digital television; interference; satellite communications; ultrawideband 

1. INTRODUCTION 

In April 2002, the Federal Communications Commission (FCC) released FCC 02-48 [1] 

legalizing intentional, low-power UWB emissions between 3.1 GHz and 10.6 GHz for 

communications devices operated indoors. UWB emissions were limited to -41 dBm average 

power in 1-MHz bandwidth and 0 dBm peak power in 50-MHz bandwidth, where average power 

is measured over a 1-millisecond integration time. Related compliance measurements require 

continuous transmissions during the measurement period. The rules define a UWB device as one 

that emits signals with 10-dB bandwidth greater than 500 MHz or greater than 20% of the center 

frequency. The FCC rules do not specify how the bandwidth requirement is achieved, 

consequently allowing industry considerable breadth in choosing a modulation. 

This breadth is exemplified by the development of Direct-Sequence (DS-UWB) and Multi-Band 

Orthogonal Frequency-Domain Multiplexing (MB-OFDM) ultrawideband technologies. 

Proponents of DS-UWB and MB-OFDM both seek standardization from IEEE (Institute of 

Electrical and Electronics Engineers) 802.15 working group 3a on high-rate (greater than 20 

million bits per second) Wireless Personal Area Networks (WPAN). As the name implies, 

 

1

 The authors are with the Institute for Telecommunication Sciences, National Telecommunications and Information 

Administration, U.S. Department of Commerce, Boulder, Colorado 80305. 
2

WPAN is intended for short-distance (nominally less than 10 meters) wireless networking of 

devices such as PCs, personal digital assistants, digital cameras, and mobile phones. 

Both DS-UWB and MB-OFDM transmitters are based on state-of-the-art integrated circuitry. 

DS-UWB modulation controls pulse polarity and hence supports phase shifting modulations. It 

achieves its ultra-wide bandwidth by transmitting sufficiently narrow pulses. MB-OFDM 

simultaneously modulates 122 carriers spaced 4.125 MHz apart to achieve its ultra-wide 

bandwidth and frequency hops the modulated carriers between non-overlapping bands. 

Since previous work performed at the Institute for Telecommunication Sciences (ITS) [2 - 5] did 

not specifically look at susceptibility of receivers to DS-UWB or MB-OFDM emissions, and 

since there is little published information regarding their interference potential, ITS entered into 

a Cooperative Research and Development Agreement (CRADA) with the Freescale subsidiary of 

Motorola, Inc. to study how interference potential of these emissions could be quantified in terms 

of signal characteristics that distinguish UWB signals. 

1.1. Purpose and Approach of This Study 

The purpose of this study is to provide data needed to predict the interference potential of various 

UWB signals. Results will be useful for regulatory agencies that are currently charged with 

defining UWB emission limits and corresponding compliance measurement procedures. Results 

will also be useful to the ultrawideband communications industry, which will only thrive if 

compliance measurement procedures fairly evaluate interference potential of all UWB signals. 

Our approach is to inject carefully characterized interference into an operating narrowband 

receiver and measure susceptibility of the receiver with precisely defined signal quality metrics. 

Data will then be analyzed to determine if there are common signal characteristics that predict 

interference potential. 

The victim receiver chosen for this interference susceptibility test is C-band satellite digital 

television (DTV). This receiver demodulates signals transmitted in the 3.7 to 4.2 GHz frequency 

range, which lies within the band allocated for UWB devices. We chose to generate the satellite 

signal in the laboratory rather than use an unpredictable live signal, to generate interference 

signals with a vector signal generator rather than rely solely on prototype devices, and to perform 

signal and system characterization measurements primarily with the vector signal analyzer to 

provide comprehensive data capable of being post-processed in many ways. 

This study has been broken down into three parts whose results will be published in three 

separate reports. The first report provides procedures to generate and characterize various 

interference signals and measure the susceptibility of a satellite DTV victim receiver to these 

signals. It provides sufficient detail to allow for thorough review and replication of the test. The 

third report will present results for various UWB signals, which will include MB-OFDM, DSUWB, and conventional pulse-position modulated pulses. 
3

The second report will present results for gated-noise interference. In August 2004, the MBOFDM Alliance Special Interest Group (MBOA-SIG) requested a waiver of FCC rules that 

require gating/hopping to be turned off during average power compliance measurements [6]. 

This group argues that UWB test procedures were developed specifically for pulse-based 

systems, putting them at an unfair disadvantage. MBOA-SIG also argues that the proposed MBOFDM system, which hops between three bands, causes no greater interference to licensed 

services than pulsed UWB devices. Opponents argue that granting the waiver will put MBOFDM at an unfair advantage as compared to other UWB systems and increase its interference 

to legacy receivers. To help resolve regulatory questions associated with non-continuous 

interference, gated Gaussian noise is included in this interference study. This signal eliminates 

the complexities associated with actual UWB modulations and provides a simple, parameterized 

theoretical basis for understanding the susceptibility characteristics. 

1.2. Organization of This Report 

This first report describes the methodology, test system, and procedures developed to 

characterize UWB emissions and measure susceptibility of C-band satellite DTV receivers. It is 

divided into five sections in the main body and eight appendices. The main body of this report is 

a comprehensive, high-level discussion providing orientation and motivation for the reader. 

Section 2 gives technical descriptions of the interference signals considered as well as discussion 

on related signal simulation and generation techniques. Section 3 examines interference signal 

characterization measurements and describes relevant metrics, test and measurement equipment, 

and procedures. Section 4 discusses technical aspects of the victim receiver. It summarizes 

relevant communication aspects of DTV systems, simulation of DTV satellite signals, and 

metrics to assess signal quality degradation. Section 5 describes a test to assess interference 

susceptibility of satellite DTV receivers to UWB signals. 

Appendices to this report contain information supporting the main body. Appendix A describes 

the mathematics behind simulation of the interference signals. Appendices B and C discuss the 

operation and specific technical aspects of the vector signal generator used to generate the 

interference signals. Appendix D explains an efficient means for estimating the amplitude 

probability distribution of a complex-baseband signal. Appendix E discusses important issues 

related to signal characterization measurements using the vector signal analyzer. Appendix F 

provides detailed test system calibration measurements. Appendix G gives hardware 

specifications. Finally, Appendix H is a glossary of acronyms and units used in this report. 
4

2. INTERFERENCE SIGNALS 

This study evaluates DS-UWB, Dithered-Pulse Ultrawideband (DP-UWB), MB-OFDM, and 

gated Gaussian noise signals. DS-UWB and MB-OFDM prototypes existed during the evaluation 

period; however, since the prototype equipment was constrained by either availability or 

functionality, we chose to develop our own methods for generating these signals. As shown in 

Figure 1, these methods required three principal tasks: signal description, computer simulation, 

and vector signal generation. This section summarizes our approach towards these tasks and 

associated challenges. 

Figure 1. Signal generation tasks. 

2.1. Signal Description 

This subsection provides brief descriptions and signal parameters of the interfering signals 

evaluated in this study; more detailed mathematical descriptions are provided in Appendix A. 

2.1.1. Direct-Sequence Ultrawideband 

DS-UWB is currently being considered as an IEEE 802.15 standard [7]. DS-UWB transmits 

signals in two bands which occupy spectrum approximately from 3.1 to 4.8 GHz and 6.2 to 9.7 

GHz. Our interest is chiefly in the lower band which overlays the C-band satellite spectrum. 

 

DS-UWB devices transmit band-limited pulses in two operational modes – sparse mode and DS 

mode; both modes are illustrated in Figure 2. Binary data modulates codewords composed of 1, 

3, 6, 12, or 24 chips. A codeword with length 1 is the trivial case where chip and codeword are 

equivalent. Lengths 3, 6, and 12 use sparse codes where all but one chip have zero values. 

Lengths 12 and 24 have a small set of direct-sequence codewords, composed of chips with 

values of 1, -1, and 0. 

Chips are generated at a nominal rate of 1320 million chips per second in the lower band. Slight 

variations of chip rate are used with length 12 and 24 direct sequence codewords to differentiate 

transmitters operating in co-located networks. Each codeword conveys 1 bit of information. 

Hence, shorter codewords have higher data rates. The data rate is reduced by the use of 

convolution codes with rates 1/2 and 3/4. As an example, a link using a 6 chip sparse codeword 

and 1/2 rate convolutional code can achieve 110 million bits per second (Mbps). 
5

Figure 2. DS-UWB construction: (a) length-6 sparse codeword (1,0,0,0,0,0), and (b) length-12 

DS codeword (0,-1,-1,-1,1,1,1,-1,1,1,-1,1). 

A single simulated DS-UWB pulse is shown in Figure 3. The root-raised cosine filter, with a 

roll-off factor of 0.3, limits the occupied spectrum to 1.716 GHz, which is 30% greater than the 

chip rate. 

Figure 3. Simulated DS-UWB pulse sampled at 23,760 million samples per second. 
6

Table 1 gives code lengths and codewords for the DS-UWB signals evaluated in this study. The 

convolutional code rate has no impact on signal characteristics and therefore is not included in 

the signal parameters that describe the permutations. Shaded rows highlight signals available 

from prototype equipment supplied by the manufacturer. 

Table 1. DS-UWB Signals Considered in This Study 

Signal Code length Codewords 

DS – 1 1 1 

DS – 2 3 0,1,0 

DS – 3 6 0,0,1,0,0,0 

DS – 4 12 0,-1,-1,-1, 1,1,1,-1,1,1,-1,1 

DS – 5 12 0,0,0,0,0,0,0,0,1,0,0,0 

DS – 6 24 -1,1,-1,-1,1,-1,-1,1,-1,0,-1,0,-1,-1,1,1,1,-1,1,1,1,-1,-1,-1 

2.1.2. Dithered-Pulse Ultrawideband 

DP-UWB is not currently being considered as an IEEE 802.15 standard. However, it is included 

in our evaluation because it is a conventional pulsed UWB signal. 

 

DP-UWB pulses are position modulated where pulse position is determined by the binary data 

and a dither factor. The dither factor is the fraction of a symbol period, Tpulse, to which the pulse 

position is bounded. This factor is often referred to as percentage dither by multiplying it by 100. 

For example, a 50% dithered pulse carrying a -1 value is bounded from the center of the symbol 

period to as much as Tpulse/4 behind the center. Similarly, the data value 1 positions the pulse 

ahead of the center of the symbol period by as much as Tpulse/4. Figure 4 depicts pulses that have 

been position modulated with 50% dithering. Pulse repetition frequencies (PRF) correspond 

directly to the data rate for binary data. This data rate would be reduced with the likely use of 

convolutional coding in a practical system. 

Figure 4. Example of 50% dithered pulses transmitting (...1, 1, -1, 1, 1, -1...). 

A single simulated DP-UWB pulse is shown in Figure 5. It is a negative unipolar pulse with an 

approximate 80-picosecond pulse width measured at a point corresponding to half the maximum 

pulse amplitude. The DP-UWB pulse frequency response is 10-dB down at 6.3 GHz. 
7

Figure 5. Simulated Gaussian pulse sampled at 24,000 million samples per second. 

Table 2 gives PRFs of the DP-UWB signals evaluated in this study. Shaded rows indicate signals 

that could be generated with a hardware DP generator constructed in lieu of a prototype. 

Table 2. 50%-Dithered-Pulse2

 UWB Signals Considered in This Study 

Signal PRF (MHz) 

DP – 1 0.1 

DP – 2 1 

DP – 3 10 

DP – 4 100 2

 Negative unipolar pulse with 80-picosecond pulse width. 

2.1.3. Multi-Band OFDM Ultrawideband 

MB-OFDM is currently being considered as an IEEE 802.15 standard [8]. MB-OFDM transmits 

in any one of fourteen bands whose center frequencies are spaced 528 MHz apart beginning at 

3.432 GHz and ending at 10.296 GHz. In this study, we are interested in the band centered at 

3960 MHz, which overlaps the C-band satellite spectrum. 

A time-frequency code, used to minimize interference between collocated networks, determines 

the order and number of bands the transmitter hops to and the number of symbol periods the 

transmitter dwells in a band. For example, a 3/2 time-frequency code would hop over 3 bands 

and dwell at each band for 2 MB-OFDM symbol periods. 
8

An MB-OFDM symbol consists of a 32-sample zero prefix, a 128-sample data block, and a 5-

sample guard interval. These 165 samples, generated at a sampling frequency of 528 MHz, last 

312.5 nanoseconds. An example of a single simulated MB-OFDM symbol is depicted in Figure 

6. Each MB-OFDM symbol conveys 100 QPSK symbols corresponding to 200 information bits. 

This throughput is reduced by the convolutional code rates 1/3, 11/32, 5/8, and 3/4. Redundant 

transmission of data on another frequency band for diversity purposes reduces the data rate 

further. A 110 Mbps rate corresponds to an 11/32 code rate with 2-band frequency diversity. 

Figure 6. Simulated MB-OFDM symbol with 32-sample zero prefix, 128-sample data block, 

and 5-sample guard interval. 

The 128-sample data block is created by inverse Fourier transforming the coefficients of 128 

tones spaced 4.125 MHz apart. The tones include 100 QPSK-modulated data tones, 12 BPSKmodulated pilot tones evenly spaced throughout the data tones, one zeroed tone at the center 

frequency, 10 BPSK-modulated guard tones near the band edges, and five zero guard tones 

located at the band edges (which do not add to the bandwidth of the signal). Figure 7 illustrates 

the tone positions. 

Figure 7. Position of zero guard tones (ZG), modulated guard tones (MG), pilot tones (P), zero 

center frequency tone (CF), and data tones (D). 
9

Table 3 gives the number of bands and dwells for MB-OFDM signals considered in this study. 

Shaded rows indicate signals available from the prototype supplied by the manufacturer. 

Table 3. MB-OFDM Signals Considered in This Study 

Signal Bands Dwell 

MB – 1 1 1 

MB – 2 3 1 

MB – 3 3 2 

MB – 4 7 1 

MB – 5 7 2 

MB – 6 7 6 

MB – 7 13 1 

MB – 8 13 2 

MB – 9 13 12 

2.1.4. Gated Gaussian Noise 

Gaussian noise is a fundamental random process present in all communication systems. It has 

been studied extensively and provides a good reference to compare other forms of interference. 

In this study, we investigated how DS-UWB statistics compare with Gaussian noise statistics. 

Gating is sometimes used in communication devices to increase signal power beyond the average 

power limits specified by the FCC. Given integration times that are large compared to a gating 

period, signal power can be amplified by the reciprocal of the duty cycle while maintaining the 

same average power. For example, if continuous Gaussian noise is gated with a 25% duty cycle 

and amplified by 6 dB, it has the same average power as the original continuous signal. 

Frequency hopping of an MB-OFDM signal appears as gating to a fixed-frequency narrowband 

receiver. To help resolve compliance measurement issues for gated/hopped signals related to [6], 

gated Gaussian noise is considered as a general interference signal with ultra-wide bandwidth 

and without the complexities associated with actual UWB modulations. Table 4 gives on-times 

and duty cycles of periodically gated Gaussian noise signals considered in this study. 

Table 4. Periodically Gated Gaussian Noise Signals Considered in This Study 

Duty cycle (percentage of time noise is gated on) GN-01 = Gaussian noise 50 25 12.5 6.25 

10 GN-02 GN-03 GN-04 GN-05 

100 GN-06 GN-07 GN-08 GN-09 

1,000 GN-10 GN-11 GN-12 GN-13 

On-time 

(nanoseconds) 

10,000 GN-14 GN-15 GN-16 GN-17 
10

Table 5 gives the number of bands and dwells for gated-noise signals that emulate the MBOFDM signals in Table 3. Two distinct gating processes are used for the insertion of prefix and 

guard intervals and for the frequency hopping due to the time-frequency codes. Both gating 

processes contribute to the percentage of time that noise is actually on. All gated-noise 

permutations in Tables 4 and 5 could be generated with a noise diode, an amplifier, and an 

electronic switch with a fast rise time. 

Table 5. Gated Gaussian Noise Signals Emulating MB-OFDM Signals in Table 3 

Signal Bands Dwells 

Percentage of 

time noise is 

actually on 

GN(MB) – 1 1 1 77.6 

GN(MB) – 2 3 1 25.9 

GN(MB) – 3 3 2 25.9 

GN(MB) – 4 7 1 11.1 

GN(MB) – 5 7 2 11.1 

GN(MB) – 6 7 6 11.1 

GN(MB) – 7 13 1 6.0 

GN(MB) – 8 13 2 6.0 

GN(MB) – 9 13 12 6.0 

2.2. Computer Simulation 

Computer simulation translates the mathematical description of the signal to samples needed by 

the vector signal generator. Simulation of realistic, wide-bandwidth signals places a considerable 

burden on most computers with finite memory and processor resources. Consequently, steps 

were taken to reduce the complexity of the wireless network system and the number of samples 

needed to accurately represent the signal produced by it. Although a prototype UWB receiver 

will not be able to demodulate the simplified signal, from the perspective of the victim receiver, 

it is indistinguishable from a signal generated by a prototype UWB transmitter. This subsection 

describes the software implementation of the interference signals at a high level; specific details 

and further explanation of the computer simulation will be provided in a separate document. 

2.2.1. Reduction of System Complexity 

The operational standards for WPAN systems are still under development [7, 8], but the 

language and structure will be similar to wireless local area networks (WLAN) as described in 

the IEEE 802.11 Handbook [9]. As depicted in Figure 8(a), wireless network systems are 

structured into two layers: medium access control (MAC) and physical (PHY). Briefly, the MAC 

layer supplies a reliable delivery mechanism for data transfer over noisy, unreliable wireless 

media. It reduces interference between systems by dictating when transmissions occur and how 

long they last. The PHY layer transmits and receives data over a shared wireless media; it is the 

interface between the MAC and wireless media. The PHY layer is divided into two sublayers: 
11

physical layer convergence procedure/protocol (PLCP) and physical medium dependent (PMD). 

The PLCP sublayer provides information needed by the receiver to demodulate the signal. The 

PMD establishes the mechanism for converting information bits into transmitted signals and 

from received signals. 

Figure 8. (a) Wireless network layers, (b) MAC frame, (c) PLCP frame. 

MAC and PLCP information is organized into frames as shown in Figures 8(b) and 8(c). The 

MAC frame includes a header, body, and a check sequence. The PLCP frame includes a 

preamble, header, and body; the PLCP body holds the MAC frame. Figure 9 depicts components 

of a typical wireless network transmitter where bits representing the PLCP frame are converted 

to signals for transmission under the control of the PMD sublayer. 

Figure 9. Transmitter PMD components. 

Levels of simulation complexity are depicted in Figure 10. The most sophisticated simulation 

includes MAC control of the PLCP frames. A less complex simulation transmits a steady stream 

of PLCP frames. The simplest simulation transmits a steady stream of scrambled MAC frames. 

Figure 10. Levels of wireless network simulation complexity. 
12

For the present scope of this study, simulation at the simplest level is adequate. As shown in 

Figure 11, random data representing the scrambled, convolutionally encoded, and interleaved 

MAC frame modulates the UWB signals described in Section 2.1 and Appendix A. Justification 

for this approach is two-fold. First, the MAC frame is the most significant part of the PLCP 

frame. Second, MAC control of PLCP frames must include consideration of various traffic 

models, which would detract from questions related to the specific UWB modulators. 

Figure 11. Simplified wireless network simulation. Figures A-1, A-2, and A-3 in Appendix A 

give specific details of DS-UWB, DP-UWB, and MB-OFDM modulators. 

2.2.2. Reduction of Samples 

For practical purposes, the number of samples and the corresponding sample rate need to be 

reduced so that reasonably long signal durations can be processed in a timely manner. For 

example, the sampling rate is often set to 4 or more times the highest frequency of interest. For 

real signals considered in this study, the highest frequency of interest exceeds 4 GHz, which 

requires more than 16 GSps. This produces far too many samples to process in a reasonable time 

for most computers. 

Sample rates of signals can be reduced by bandpass filtering the real signal and converting the 

real-bandpass signal to complex-baseband (CBB) as depicted in Figure 12. Conversion to CBB 

allows for the use of a sample rate that depends on the bandwidth, B, of the filter rather than 

highest frequency of the original signal. Simulation in CBB also has the benefit of generating 

waveforms readily implemented on the vector signal generator (VSG). 

Figure 12. Spectrum amplitude of (a) real-bandpass, (b) analytic, and (c) CBB signals. 
13

2.3. Implementation on the Vector Signal Generator 

Simulated CBB interference waveforms were implemented on the VSG. A brief description of 

the VSG is given here with a more detailed description given in Appendix B. Figure 13 shows a 

highly simplified block diagram of the VSG. The simulated discrete-time CBB signal, stored in 

fixed-point format as a waveform file, is loaded into VSG memory. The VSG converts the digital 

in-phase (I) and quadrature-phase (Q) signals to I and Q analog signals using interpolation, 

digital-to-analog conversion, and analog reconstruction filtering. The analog I and Q signals are 

the inputs to the I/Q modulator, which converts the CBB signal to a real signal at the RF center 

frequency determined by the local oscillator (LO). 

Figure 13. Vector signal generator block diagram. DAC stands for digital-to-analog converter. 

The VSG produces an RF output that is somewhat degraded. Imperfect digital-to-analog 

conversion distorts the desired signal. This distortion is most severe at the band edges. 

Additionally, LO feed-through, intermodulation products, and other spurious responses from 

imperfect I/Q modulation can corrupt the desired signal. Appendix B discusses techniques to 

minimize the amplitude of these undesirable signals. Appendix C provides a detailed discussion 

of the most prominent VSG impairment, LO feed-through, and describes an experiment that 

demonstrates that LO feed-through is not a problem for the DTV interference susceptibility test. 
14

3. INTERFERENCE CHARACTERIZATION MEASUREMENTS 

Interference characterization measurements were made with the vector signal analyzer (VSA), 

spectrum analyzer (SA), digital oscilloscope (DO), and power meter. These measurements were 

made at C-band on signals directly out of the VSG. Measurements were also performed on 

prototypes when available. This section describes metrics used to characterize interference, data 

needed to compute the metrics, instruments used for collecting the data, and specific settings for 

the instruments. 

3.1. Metrics and Data Needed to Compute Them 

Interference signals can be characterized by statistics of the random variable representing the 

time-varying signal voltage. Measurement and statistical processing is made difficult by the 

high-frequency content of UWB signals. For example, DS-UWB and DP-UWB pulses shown in 

Figures 3 and 5 require sample rates of 23.76 GSps and 24 GSps, respectively, to obtain 

adequate resolution. Currently, digitization at these sample rates requires state-of-the-art 

oscilloscopes. Fortunately, our interest is in the effect of interference on relatively narrowband 

receivers. Inside a superheterodyne victim receiver, interference is band-limited and 

downconverted to CBB along with the desired signal. Consequently, it makes sense to measure 

and statistically analyze the CBB signal, which can be sampled at slower rates. 

Statistics of particular interest are peak voltage, root-mean-square (rms) voltage, amplitude 

probability distribution (APD), power spectral density (PSD), and other higher-order statistics 

[10]. APD characterizes a random process by displaying the percentages of time that a signal 

exceeds certain levels, which can be determined by histogramming or sorting the amplitudes. 

The sorting method for estimating APDs is described in Appendix D. PSD is a measure of the 

average power per unit bandwidth as a function of frequency. It can be determined from the 

Fourier transformed autocorrelation function or from averaged spectrum magnitudes. 

3.2. Description of Test and Measurement Equipment 

Previous UWB signal characterization studies [2, 3] relied mostly on the spectrum analyzer, 

power meter, and digital oscilloscope. In addition, this study utilizes the vector signal analyzer to 

acquire amplitude and phase samples of the CBB signal. These instruments differ in bandwidth, 

dynamic range, and the type of data they acquire. This subsection describes each instrument and 

explains tradeoffs associated with each. Measurement parameters are described for acquiring 

data necessary to compute metrics that characterize interference signals. 

3.2.1. Vector Signal Analyzer 

The Agilent 89461A VSA provides approximately 36-MHz maximum bandwidth with 50-dB 

dynamic range in the maximum bandwidth. In Appendix E, dynamic range is defined and 

measured. Figure 14 shows a simplified block diagram of the VSA. Signals at the RF input are 
15

attenuated to prevent overload and filtered to reject image frequencies. The RF signal is then 

downconverted to a final intermediate frequency (IF) of approximately 19 MHz. The final IF 

signal is converted to a digital IF signal by an analog-to-digital converter (ADC) operating at 95 

million samples per second (MSps). The digital IF signal is converted to CBB using digital 

downconversion. The fastest sampling rate of the I and Q signals is 46.08 MHz corresponding to 

a 21.7-nanosecond sample period and a 36-MHz span. For the VSA, span refers to the bandwidth 

where amplitude and phase distortion are negligible. Decimation to slower “cardinal” sampling 

rates, each related by a factor of 1/2, can be used to generate other cardinal spans. For example, 

cardinal spans of 18, 9, 4.5, 2.25, 1.125, and 0.5625 MHz correspond to decimation factors of 

1/2, 1/4, 1/8, 1/16, 1/32, and 1/64. 

Figure 14. Conceptual block diagram of a vector signal analyzer. 

VSA operation is controlled by vendor-supplied software running on a PC. VSA measurement 

settings include center frequency, span, input range (i.e., maximum signal amplitude), and 

number of samples. The software commands the VSA to acquire data at the smallest cardinal 

span greater than the requested span. Data is transferred to the PC and re-sampled to a rate 

corresponding to the requested span. The re-sampled data can be saved in memory for postmeasurement analysis or processed for immediate display. For a more detailed discussion of the 

VSA refer to Agilent Application Note 150-15 [11]. 

The VSA is good for characterizing interference to DTV receivers, since its bandwidth is 

commensurate to that of the satellite transponder. Furthermore, the VSA provides data with both 

amplitude and phase. This improves the versatility of the data and reduces the number of 

measurements required. For example, we are interested in illustrating signal characteristics in a 

variety of bandwidths corresponding to the satellite transponder, i.e., 36 MHz, the DTV signal 

used for interference susceptibility tests, i.e., 19.51 MHz, and other legacy systems (to 

investigate bandwidth scalability of statistics). Rather than remeasure at each bandwidth, digital 

filters are applied during post processing to achieve the specified processing bandwidths; this 

would not be possible if only magnitude data were available. 

VSA settings for characterizing interference signals are given in Table 6. Settings were chosen 

with three considerations in mind. First, the maximum VSA bandwidth is used to characterize 

interference to the satellite DTV channel. Second, it is preferable to use a cardinal span, rather 

than some arbitrary span, to avoid re-sampling by the vendor supplied software. Finally, at least 

100,000 samples, free of correlations introduced by measurement equipment, are necessary to 

estimate statistics at the 0.01 percentile. Hence, at least 100,000 × 36/10 = 360,000 samples 

should be measured in the 36-MHz span, 100,000 × 4.5/1 = 450,000 samples should be measured 

in the 4.5-MHz span, and 100,000 × 0.5625/0.1 = 562,500 samples should be measured in the 

0.5625-MHz span. 
16

Table 6. VSA Measurement Settings for Various Post-Processing Bandwidths 

Post-Processing 

Bandwidths (MHz) Span (MHz) Number of Samples 

Center 

Frequency 

(MHz) 

Input Range 

(dBm) 

36, 19.51, 10 36 > 360,000 

1 4.5 > 450,000 

0.1 0.5625 > 562,500 

3820 -30 

Center frequency corresponds to the center of the DTV channel chosen for the interference 

susceptibility test. An input range of -30 dBm was chosen for these measurements. VSA 

measurement accuracy is only possible if the range of signal amplitudes resides within the 

dynamic range of the measurement device. Appendices E and F demonstrate how to set the 

average power of input signals to ensure accurate VSA measurements. 

3.2.2. Spectrum Analyzer 

The Agilent E4440A spectrum analyzer provides an 8-MHz maximum bandwidth with more 

dynamic range than the VSA. Figure 15 shows a simplified block diagram of the SA. The signal 

at the RF input is attenuated and passed through a preselection filter to reject strong out-of-band 

signals. The sweep generator instructs the SA to sweep over a specified frequency span, which 

shifts the RF input to a target IF for the two-stage downconverter. The final IF signal is centered 

at 7.5-MHz and converted to a digital IF signal by the analog-to-digital converter operating at 30 

MSps. The digital IF signal is then converted to CBB using a digital downconversion and 

decimation process. At this point, the complex samples can be processed and displayed, or sent 

to a PC for post measurement analysis. For our purposes, digital signal processing inside the SA 

implements traditional swept measurements that involve application of a resolution bandwidth 

(RBW) filter, envelope detector, and video bandwidth (VBW) filter; conventional metrics, i.e., 

peak and root-mean-square (rms) amplitude, are displayed. 

Figure 15. Conceptual block diagram of a digital spectrum analyzer. 
17

SA measurement settings include center frequency, span, RBW, VBW, number of frequency steps 

(Nf), sweep time, and detector type. Center frequency and span define the frequency range the 

SA sweeps across. The sweep rate and resolution are determined by the span, number of 

frequency steps, and the length of time allotted for one sweep. For more in-depth discussion of 

spectrum analyzer operations, the reader is referred to Agilent Application Note 150 [12]. 

The maximum SA bandwidth is smaller than that of the satellite DTV channel; however, SA 

measurements were used to provide PSD and peak amplitude estimates, verify VSA 

measurements, perform FCC compliance tests, and monitor signals. Table 7 gives SA settings for 

characterizing interference signals. Measurements at 3950 MHz are centered on the C-band 

satellite spectrum. Measurements at 3820 MHz are centered on the DTV channel used for 

susceptibility testing. Frequency step size, i.e., span divided by the number of frequency steps, is 

set to one sixth of the RBW to ensure that spectrum details are not missed. VBW is much wider 

than RBW to ensure peak amplitude accuracy. Sweep time is set to 1000 × Nf × k/RBW seconds 

to ensure rms amplitude accuracy. The Gaussian RBW filters require k = 3. 

Table 7. Spectrum Analyzer Settings for Characterizing Interference Signals 

Center 

Frequency 

(MHz) 

Span 

(MHz) 

RBW

(MHz) 

VBW

(MHz) Nf

Sweep 

Time 

(seconds) 

Detector 

3950 600 1 3600 10.8 

3820 40 0.1 50 2400 72.0 Rms, peak 

3.2.3. Digital Oscilloscope 

The Tektronix TDS7704B digital oscilloscope DO provides real signal samples with 7-GHz 

maximum bandwidth and less dynamic range than the VSA and SA. Figure 16 shows a block 

diagram of the DO. It performs anti-alias filtering and digitizes the signal at a maximum of 20 

GSps. Trigger functions allow the user to control when digitization begins. DO measurement 

settings include sample rate, time scale, voltage scale, and number of samples. 

Figure 16. Conceptual block diagram of a digital oscilloscope.

The wide bandwidth of the DO allows for a qualitative “snapshot” of the full-band interference 

signals to reveal signal parameters such as pulse rise time, signal duration, and gating period. 
18

Table 8 gives typical settings for DO measurements. Actual settings depend on the specific 

signal characteristics of interest. Maximum sample rate is used to ensure the highest resolution. 

Table 8. Digital Oscilloscope Settings for Characterizing Interference Signals 

Time Scale 

(microseconds/div) 

Voltage Scale 

(millivolts/div) 

Sample Rate 

(GSps) 

40 

4 50 20 

3.2.4. Power Meter 

The Agilent 4419B power meter with the 9300A diode average power sensor provides average 

power measurements with 18-GHz maximum bandwidth and 80-dB dynamic range. Operating in 

a normal mode, the power meter provides 20 average power measurements per second. 

This power meter provides calibrated average power measurements of both RF and IF signals. 

To get the most accurate reading, it is set to the center frequency of the measured signal and 

1024 measurements are averaged; this takes approximately 60 seconds to complete. 
19

4. VICTIM RECEIVER 

C-band satellite television was chosen as the victim receiver for this interference susceptibility 

test because C-band receivers operate within the band allocated for UWB emissions, satellite 

signals are weak and vulnerable to interference, and a number of operational scenarios can be 

investigated. This section discusses relevant communication aspects of satellite DTV systems, 

simulation of the DTV satellite signal, and metrics to assess quality; a more general discussion of 

satellite communication techniques can be found in Pratt, et al. [13] and Couch [14]. 

4.1. Satellite Digital Television Description 

The scope of this study is limited to C-band satellite DTV transmissions that comply with the 

commonly used Digital Video Broadcast (DVB) recommendations ETS 300 421 [15] published 

by the European Telecommunications Standards Institute (ETSI). This subsection discusses the 

communication aspects of a single transponder channel for satellite DTV systems that comply 

with DVB recommendations. 

4.1.1. MPEG-2 Encoder 

DTV video and audio signals are digitized separately, compressed, and then multiplexed into a 

serial bit stream for transmission. Compression algorithms are applied to minimize the amount of 

data required to represent the video image or audio, while preserving the level of quality required 

for the given application. This is achieved by matching spectral characteristics of video and 

audio signals to the frequency response of human eyes and ears; further compression is achieved 

by not transmitting static information. DVB uses the MPEG-2 standard for compression. 

There are two levels of multiplexing in DVB. At the lowest level, components that make up a 

program (e.g., video, audio, system-control information) are multiplexed. At a higher level, the 

programs themselves are multiplexing. The transport subsystem is based on fixed-length packets, 

each holding 188 bytes of information which represents one MPEG segment. The contents and 

timing of each packet are identified by packet headers. 

4.1.2. Forward Error Correction and Modulation 

DVB systems mitigate errors by reducing the baud rate and protecting the signal with forward 

error correction codes (FEC). Two FEC codes, Reed-Solomon (RS) and convolutional, are used. 

Briefly, the RS code has a block length of 204 bytes carrying 188 information bytes, or one 

MPEG segment, and can correct up to 8 byte errors per block. The convolutional code has length 

7 and rates that include 1/2, 2/3, 3/4, 5/6, and 7/8. Interleaving (with a depth of 12 bytes) is used 

to shield the Reed-Solomon decoder from burst errors. FEC-coded bits are mapped into QPSK 

symbols for transmission. Symbols are band-limited by a root-raised-cosine (RRC) filter with a 

roll-off factor (α) of 0.35 and upconverted to the satellite channel radio frequency (RF). 
20

Table 9 gives Eb/No and signal-to-noise ratio (SNR) performance requirements for DVB satellite 

television. Eb/No is the ratio of energy per bit over noise power spectral density. SNR

requirements are 3 dB higher over Eb/No because QPSK maps two bits into one symbol. If the 

received signal is above the specified Eb/No, the specified FEC techniques are designed to 

provide a bit-error rate (BER) between 10-10 and 10-11 at the output of the RS decoder. This 

corresponds to less than one uncorrected error “event” per transmission hour. (Although ETS 300 

421 [15] is unclear as to the nature of an error “event,” it is assumed to correspond to an MPEG 

segment decoding error.) 

Table 9. Requirements for One Uncorrected Error “Event” per Transmission Hour [15] 

Convolutional Inner Code 

Rate 

Eb/No

 3 Requirement (dB) 

for 10-11 ≤ BER < 10-10

After the RS decoder

Corresponding SNR

Requirement (dB) for 

QPSK 

1/2 4.5 7.5 

2/3 5.0 8.0 

3/4 5.5 8.5 

5/6 6.0 9.0 

7/8 6.4 9.4 

3 Eb/No values include a 0.8-dB modem implementation margin and the 0.36-dB noise bandwidth increase 

due to the RS code implementation. 

4.1.3. Receiver 

Figure 17 depicts the two-stage-downconversion superheterodyne architecture typically utilized 

by satellite DTV receivers. Immediately after the antenna feed, a low-noise block downconverter 

(LNB) amplifies the received signal and downconverts it to the first IF between 950 and 1450 

MHz. The signal is carried to the receiver via coaxial cable, where it is downconverted once 

more, channel selected, and demodulated. The QPSK demodulator uses RRC filtering to 

minimize intersymbol interference and maximize SNR. The demodulated signal is applied to the 

Viterbi decoder, de-interleaved, and RS decoded. Finally, error-free MPEG segments are 

demultiplexed, decompressed, and delivered to the television set for viewing. Segments received 

in error are not passed on. Error concealment is used to hide the effects of their absence. 

Figure 17. Signal quality metrics measured at different points in a DTV receiver. 
21

4.2. DTV Quality Metrics 

Video and audio quality is often subjectively evaluated by viewer and listener panels briefly 

exposed to received images and sounds. Acquisition of subjective quality metrics is qualitative, 

time consuming, and prone to perceptual error because there are a wide variety of techniques 

used by receivers to conceal the effects of MPEG segment errors. 

Alternatively, quantitative signal quality metrics from various points in the receiver are available 

from DTV signal monitoring instruments. In this study, signal quality metrics from the Tektronix 

MTM400 DVB signal monitor are recorded to evaluate degradation of the victim receiver. 

Available metrics are modulation error rate (MER), error vector magnitude (EVM), pre-Viterbi 

bit-error rate (BER), and cumulative transport error flag (TEF) from which MPEG segment error 

rate (SER) can be computed. Figure 17 shows where the metrics are taken from within the 

receiver signal processing chain. 

MER and EVM are taken after I/Q demodulation. An example of a demodulated QPSK 

constellation and its I and Q components are shown in Figure 18. 

Figure 18. QPSK constellation with both target and received symbols. 

Modulation error ratio in dB is defined from these components by 

where N data points are taken to achieve sufficient accuracy. For QPSK, EVM can be expressed 

in terms of MER as 2 / 20 10 MER EVM − = . 

( )

( )

10 log ,

1

2 2

1

2 2

⎟

⎟

⎟

⎟

⎠

⎞

⎜

⎜

⎜

⎜

⎝

⎛

+

+

=

∑

∑

=

=

N

n

n n

N

n

n n

I Q

I Q

MER

δ δ
22

SER is post-Reed-Solomon MPEG segment error rate and is calculated from TEF and 

Coordinated Universal Time (UTC) as 

where Rseg is the MPEG segment rate with units of MPEG segments per second. 

4.3. Operational Scenario Chosen for DTV Interference Susceptibility Tests 

The operational scenario of the victim receiver chosen for this study is shown in Table 10; it 

corresponds to DTV channel 581 from geostationary satellite Satcom C3 positioned 

approximately 35,786 km above the equator at 131.0° W longitude. Modulation, compression, 

and error correction schemes comply with DVB recommendations described in Section 4.1. 

Table 10. Satellite Simulation Scenarios for DTV Interference Susceptibility Tests 

Transponder Center Frequency 3820 MHz 

Modulation QPSK with root-raised cosine filter (α = 0.35) 

Compression MPEG-2 

Data Rate 26.970353 Mbps 

Symbol Rate RS = 19.510468 Mbaud 

Reed-Solomon Error Correction RRS = 188/204 

Convolutional Error Correction Rconv = 3/4, K = 7

Interleaving Depth = 12 bytes 

Signal-to-Noise Ratio {8.5, 11.5, 14.5} dB 

Calculation of the operational segment rate begins at the byte level. 188 information bytes are 

packed into a Reed-Solomon codeword, interleaved, and convolutionally encoded. Overhead 

associated with the error correction scheme is quantified by the rates RRS = 188/204 and Rconv = 

3/4. The length in bits of one MPEG segment is 

and the rate at which segments are transmitted is 

where 2m is the modulation alphabet size and RS is the symbol rate. For the operational scenario, 

m = 2 for the QPSK alphabet, RS = 19.51 Mbaud, and Rseg = 17,932 segments per second 

corresponding to Tseg = 55.76 microseconds per segment. The symbol rate and RRC roll-off 

factor correspond to an occupied bandwidth of approximately 19.51 × (1+0.35) = 26.34 MHz. 

, ( )

( ) ( )

2 1

2 1

Rseg t t

TEF t TEF t SER −− =

segment

bits 2176

byte

bits 8 188 = × =

RS conv

seg R R

l

, S

seg

seg R m R

l =
23

SNR is determined primarily by the size of the receiving dish, the noise figure of the LNB, and 

the path length of the signal. Three values, separated by 3 dB, were chosen to include a 

reasonable set of satellite transmission scenarios; 8.5-dB SNR corresponds to the minimum 

requirement for one uncorrected error event per hour [15]. 

4.4. Satellite Signal Simulation 

Complexity is built into satellite television systems to increase capacity and improve quality of 

the broadcast. Transponders onboard geostationary satellites capture uplink signals and retransmit the signals back down to Earth. A typical satellite has twelve 36-MHz transponders. 

Some satellites double the number of channels by using horizontal and vertical polarizations. The 

transmitted satellite signal propagates down through the atmosphere and is received by dualpolarized, high-directivity dish antennas. 

For the purpose of quantifying interference susceptibility, receiving a “live” satellite signal 

presents significant disadvantages. Interference can be introduced by the cross-polarized channel 

on the same frequency, the co-polarized signal on the adjacent channel, and devices located near 

the receiving antenna. The signal is weakened by attenuation and depolarization effects of the 

atmosphere. Distortion is caused by nearby ground reflections. Also, “live” video content can be 

inconsistent and unpredictable. These problems are alleviated by simulating the satellite signal 

and coupling that signal, along with the simulated interference signal, to the victim receiver with 

a coaxial cable and a directional coupler rather than a pair of antennas. 

Figure 19 shows a block diagram for the satellite signal generator used in this study. The digital 

video stream is SMPTE-259M, the native video format for MPEG-2, and therefore provides the 

highest quality input signal. The video clip used is “Flower Garden” from the International 

Telecommunication Union Recommendation ITU-R BT.802.1 [16]. This clip is 20 seconds long 

and repeats itself forward and then backward to minimize discontinuity. The steady scan and 

detailed video content of “Flower Garden” provides a scene with lots of spatial and temporal 

information that constantly forces the encoder to generate new digital video. The encoder creates 

a 26.97-Mbps MPEG-2 bit stream, the modulator adds FEC and creates a 19.51-Mbaud QPSKmodulated signal centered at 70 MHz, and two upconversion stages shift the signal to 3820 

MHz. Details on the upconverter are provided in Appendix F. 

Figure 19. Block diagram for the satellite signal generator. Hardware specifications are in 

Appendix G. 
24

5. INTERFERENCE SUSCEPTIBILITY TESTS 

This section provides technical details and justification for tests to assess ultrawideband 

interference susceptibility of C-band satellite DTV receivers. 

5.1. Methodology 

DVB error correction and concealment schemes cause steep receiver performance curves where 

a 1-dB difference in SNR can cause abrupt changes in picture quality. For our operational 

scenario, the received picture is seemingly perfect at an SNR of 6 dB, yet totally blanked by 

errors at 4 dB. To resolve this abrupt change in performance, the test system and procedure were 

designed for precision and repeatability. 

The developed test system: 

• simulates interference signals in software, so the widest range of UWB signal parameters 

are available for testing; 

• generates interference signals with a VSG from simulated waveforms, so interference 

amplitude is precisely controlled; 

• characterizes interference signals with a VSA, which provides both signal amplitude and 

phase information for post-measurement filtering and analyses; 

• simulates the satellite signal, so that scene content and SNR are controlled; 

• conductively couples signals to the satellite receiver, so uncertainties introduced by radio 

channel interference, attenuation, and distortion are eliminated; 

• evaluates performance degradation quantitatively with signal quality metrics from an 

MPEG stream monitor rather than subjective quality metrics; 

• is completely characterized, so that its effects on the test are known. 

Also, the test procedure: 

• is under software control to eliminate procedural errors; 

• calibrates the measurement system at the beginning of each test to ensure consistency 

and system integrity. 

5.2. Test System 

A block diagram of the measurement system is shown in Figure 20. Characterization 

measurements and component specifications are provided in Appendices F and G. The system is 

comprised of five subsystems: (1) interference signal generation, (2) satellite signal generation, 

(3) coupling, (4) victim receiver, and (5) calibration. 

Interference signals are generated from software simulated waveforms with a VSG. The VSG 

provides 0.01-dB amplitude control and is capable of completely turning off the interference 

signal and terminating the port in 50 ohms. Satellite signals are simulated and step variable 

attenuator VA provides 1-dB control of the satellite SNR. A switch was provided that can 

completely shut the satellite signal off and terminate the port in 50 ohms during calibration. 
25

Figure 20. Block diagram for DTV interference susceptibility test. System calibration 

measurements are in Appendix F. Hardware specifications are in Appendix G. 

Satellite and interference signals are added via the coupling subsystem. The interfering signal is 

applied to the port of directional coupler DCPL-1 that attenuates the signal by 10 dB. The 

bandwidth of bandpass filter BPF1 corresponds to that of the satellite transponder channel. 

Lowpass filter LPF assists BPF1 in removing unwanted mixing products introduced by the 

satellite simulator upconverter. These filters were not included in the satellite simulation 

subsystem because they also band-limit the interfering signal. Limiting the bandwidth of the 

interfering signals ensures that performance degradation is due to co-channel interference rather 

than LNB compression due to out-of-band interference. It also ensures that signals generated by 

other VSGs or prototype devices will have the same interference bandwidth. 

In the receiver subsystem, the LNB downconverts signals to L-band. A bias-tee provides the 

LNB with the necessary dc voltage and impedance match. The MPEG stream monitor provides a 

pass-through, L-band signal for the satellite receiver. Scenes transmitted by the satellite 

simulator can be viewed on a video monitor attached to the satellite receiver. Switch-2 enables 

the signal to be directed to either the MPEG stream monitor or the calibration subsystem. 

In the calibration subsystem, measurements made during each test are performed with the VSA 

and power meter. The SA is used for signal monitoring and diagnostics. Bandpass filter BPF2 is 

used to limit the bandwidth of the noise amplified by the LNB and measured by the power meter. 

BPF2 bandwidth is much wider than that of BPF1 and the VSA, so it does not influence VSA 

calibration measurements. Care was taken to ensure that VSA measurements are made within the 

dynamic range of the system and measurement instrument (see Appendix E). 
26

5.3. Procedures 

A single DTV interference susceptibility test, for any one interference signal given in Section 

2.1, involves system calibration followed by quality-metric acquisition. 

5.3.1. Calibration 

At the beginning of each test, the system is calibrated to ensure that all hardware is operating 

properly and the satellite signal and interference amplitudes are set properly. The calibration 

procedure consists of setting the amplitude of the satellite simulator to a level that corresponds to 

the test SNR and the amplitude of the interference signal to a level that affords an accurate VSA 

measurement. Measurements of the satellite signal plus system noise, interference plus system 

noise, and system noise alone are then made with the VSA and power meter. These 

measurements are used in post-measurement processing to precisely determine SNR and 

interference-to-noise ratio (INR) conditions present during the test. 

5.3.2. DTV Quality Metric Acquisition 

DTV quality metrics are acquired with the following procedure at discrete interference levels: 

1. Interference is turned off and the receiver is allowed to acquire and demodulate the 

desired satellite signal in the absence of interference for 20 seconds. 

2. Interference at the specified amplitude is applied for approximately 200 seconds. 

3. During the time of interference exposure, MER, EVM, BER, TEF, and UTC time are 

sampled at about 3 samples per second and written as a single record to a data file. 

During coarse-resolution acquisition, this cycle is repeated at increasing interference amplitudes 

separated by 2-dB intervals, beginning at an interference level that has negligible effect on signal 

quality and ending where SER is in excess of 10-1. The fine-resolution acquisition begins 2 dB 

below the interference level where the first TEF was counted in the coarse-resolution acquisition. 

Amplitude is incremented in 0.1-dB steps until 10-1 SER is once again achieved. 

Data from the fine-resolution acquisition is used to determine the “threshold of visibility,” i.e., 

SER where degradation is first perceived. Video quality studies have shown that this threshold 

correlates to approximately SER ≈ 10-4 [17]. From the standpoint of a Bernoulli experiment, 

where independent interference samples degrade reception of independent MPEG segments, 200 

seconds of applied interference is adequate for estimating the SER. 
27

6. SUMMARY

This report gives procedures for statistical characterization of UWB interference signals and 

assessment of interference susceptibility of C-band satellite television receivers. Our approach is 

to inject carefully characterized interference into an operating narrowband receiver and measure 

the susceptibility of the receiver with precisely defined signal quality metrics. 

The victim receiver chosen for this interference susceptibility test is C-band satellite digital 

television. This receiver demodulates signals transmitted in the 3.7 to 4.2 GHz frequency range, 

which lies within the band allocated for UWB devices. We chose to generate the satellite signal 

in the laboratory rather than use an unpredictable live signal, to generate interference signals 

with a vector signal generator rather than rely solely on prototype devices, and to perform signal 

and system characterization measurements primarily with the vector signal analyzer to provide 

comprehensive data capable of being post-processed in many ways. 

Specifically, interference signals are simulated in software (see Section 2.2 and Appendix A), so 

the widest range of UWB signal parameters are available for testing, and generated with a VSG 

(see Section 2.3 and Appendix B), so interference amplitude is precisely controlled. 

Characterization of the interference signals with a VSA (see Section 3.2.1) provides both signal 

amplitude and phase information for post-measurement filtering and analyses. Satellite signal 

simulation (see Section 4.4) provides control over the scene content and SNR. Signals are 

conductively coupled to the satellite receiver to eliminate uncertainties introduced by radio 

channel interference, attenuation, and distortion. Signal quality metrics, i.e., MER, EVM, BER, 

and SER, from an MPEG stream monitor (see Section 4.2) are used to evaluate performance 

degradation quantitatively. Finally, the test procedure is under software control to eliminate 

procedural errors and calibrates the measurement system at the beginning of each test to ensure 

consistency and system integrity. 

Comprehensive data acquired will be analyzed to determine if there are common signal 

characteristics that predict interference potential. Results will be useful for regulatory agencies 

that are currently charged with defining UWB emission limits and corresponding compliance 

measurement procedures. Results will also be useful to the communications industry that will 

only thrive if compliance measurement procedures fairly evaluate interference potential of all 

UWB signals. 
28

7. ACKNOWLEDGMENTS

The authors recognize John McCorkle of Freescale, Inc. for his leadership, insight, and 

insistence on the highest quality. We learned a great deal from working with John in all relevant 

technical areas. Dr. Paul Runkle of Signal Innovations provided a wealth of expertise in the field 

of digital signal processing. Given that signal processing was the crux of this project, Paul made 

significant contributions by ensuring the integrity and efficient execution of our simulations. 

This investigation required a number of ITS engineers with a broad range of expertise, some of 

whom are not included in the list of authors. The authors recognize J. Randy Hoffman, Richard 

Statz, and Steven Engelking for developing much of the low level software routines needed to 

automate the test; Stephen Wolf for his expertise in video quality assessment; Frank Sanders for 

sharing knowledge learned in previous interference tests on C-band earth station receivers [18]; 

Dr. Roger Dalke for his knowledge of random processes and experimental uncertainty analyses, 

and his critical eye; Andrew Rogers and Michael Mitchell for their attention to detail in hardware 

construction and signal characterization measurements; and Dr. William Kissick for his support 

and leadership. 
29

8. REFERENCES 

[1] “First Report and Order in the matter of ET Docket 98-153 (Revision of Part 15 of the 

Commission’s Rules Regarding Ultra-Wideband Transmission Systems),” FCC 02-48, 

adopted Feb. 14, 2002, released Apr. 22, 2002. 

[2] W.A. Kissick, Ed., “The temporal and spectral characteristics of ultrawideband signals,” 

NTIA Report 01-383, Jan. 2001. 

[3] J.R. Hoffman, M.G. Cotton, R.J. Achatz, R.N. Statz, and R.A. Dalke, “Measurements to 

determine potential interference to GPS receivers from ultrawideband transmission 

systems,” NTIA Report 01-384, Feb. 2001. 

[4] J.R. Hoffman, M.G. Cotton, R.J. Achatz, and R.N. Statz, “Addendum to NTIA Report 01-

384: Measurements to determine potential interference to GPS receivers from 

ultrawideband transmission systems,” NTIA Report 01-389, Sep. 2001. 

[5] J.R. Hoffman, E.J. Haakinson, and Y. Lo, “Measurements to determine potential 

interference to public safety radio receivers from ultrawideband transmission systems,” 

NTIA Report 03-396, Apr. 2003. 

[6] “Office of Engineering and Technology Declares MBOA-SIG Request for a Waiver of Part 

15 for an Ultra-Wideband System to be a “Permit-but-Disclose” Proceedings for Ex Parte 

Purposes,” Public Notice, ET Docket No. 04-352, DA 04-2793, released Sep. 3, 2004. 

[7] R. Kohno, M. Welborn, and M. McLaughlin, “DS-UWB Physical Layer Submission to 

802.15 Task Group 3a,” IEEE P802.15-04/0137r0, Mar. 2004. 

[8] A. Batra, et al., “Multi-band OFDM Physical Layer Proposal for IEEE 802.15 Task Group 

3a,” IEEE P802.15-03/268r3, Mar. 2004. 

[9] B. O’Hara and A. Petrick, IEEE 802.11 Handbook, New York, NY: IEEE Press, 1999. 

[10] C.L. Nikias and A.P. Petrotulu, Higher-Order Spectra Analysis, Upper Saddle River, NJ: 

PTR Prentice Hall, 1993. 

[11] Agilent, “Vector Signal Analysis Basics,” Application Note 150-15. 

[12] Agilent, “Spectrum Analyzer Basics,” Application Note 150. 

[13] T. Pratt, C. Bostian, and J. Allnutt, Satellite Communications, John Wiley and Sons, 2003. 

[14] L.W. Couch, Digital and Analog Communication Systems, New York, NY: Macmillan 

Publishing Company, 1987. 

[15] ETS 300 421: “Digital broadcasting systems for television, sound, and data services: 

Framing structure, channel coding, and modulation for 11/12 GHz satellite services,” 

European Telecommunications Standards Institute, Dec. 1994. 
30

[16] ITU-R BT.802.1, “Test Pictures in Sequences for Subjective Assessments of Digital 

Codecs Conveying Signals Produced According to Recommendation ITU-R BT.601,”

International Telecommunication Union, Jul. 1994. 

[17] J.C. Whitaker, “Digital television transmission systems,” Ch. 13 in Standard Handbook of 

Video and Television Engineering, J.C. Whitaker, Ed., New York, NY: McGraw-Hill, 

2000. 

[18] F. Sanders, “Measurements of pulsed co-channel interference in a 4-GHz digital earth 

station receiver,” NTIA Report 02-393, May 2002. 

[19] ETS TR 101 290: “Digital video broadcasting (DVB); Measurement guidelines for DVB 

systems,” European Telecommunications Standards Institute, v1.2.1, 2001. 

[20] J.W. Modestino and B. Sankur, “Modelling and analysis of impulsive noise,” Part VII, 

Section 6 in Communication Systems and Random Process Theory, J.K. Skwirzynski, Ed., 

Alphen aan den Rijn, The Netherlands: Sijthoff & Noordhoff, 1978. 

[21] Agilent Technologies, Inc., Application Note 57-2, “Noise Figure Measurement Accuracy 

– The Y-Factor Method,” Mar. 19, 2004. 
31

APPENDIX A: MATHEMATICAL SIGNAL DESCRIPTION 

This section provides mathematical expressions for u(t), i.e., the real-valued representation of 

interference signals pertinent to this study. 

A.1. Direct-Sequence Ultrawideband 

A block diagram of the simulated direct-sequence ultrawideband (DS-UWB) transmitter is 

shown in Figure A-1. 

Figure A-1. Simulated DS-UWB transmitter block diagram. 

The simulated DS-UWB signal is mathematically described by 

where dk is the k

th random data element with values {1, -1}, L is the codeword length with values 

of {1, 3, 6, 12, and 24}, cl is the l

 th codeword chip with values {1, 0, -1}, and Tchip is the chip 

period. 

The impulse response of the pulse shaping filter is 

where hrrc(t) is the impulse response of the root-raised-cosine (RRC) filter and fchip is the chip 

rate. RRC filters are often specified by their frequency response 

( ) ( ) ( ) 1 , 1

∑ ∑

∞

= = −∞

= − − − k

L

l

k l DS chip Tchip u t d c p t kLT l

p (t) h (t)sin(6 f t) , DS = rrc π chip
32

where fco is the filter cutoff frequency equal to fchip/2 and α is the roll-off factor. 

A.2. Dithered-Pulse Ultrawideband 

A block diagram of the simulated dithered-pulse ultrawideband (DP-UWB) transmitter is shown 

in Figure A-2. 

Figure A-2. Simulated DP-UWB block diagram. 

The simulated DP-UWB signal is mathematically described by 

where pDP is the pulse-shaping filter impulse response, Tpulse is the average pulse repetition 

period, and dk is the k

th random data element with values {1, -1}. βk is the kth dithered position 

relative to Tpulse, which is uniformly distributed with a probability density 

where βmax is the dither fraction, which is a constant bounded by 0 < βmax ≤ 1. 

[ ] ( ) ( ) ⎪

⎪

⎪

⎩

⎪

⎪

⎪

⎨

⎧

− < ≤ +

+ − −

> +

≤ −

=

(1 ) (1 ) 2

1 cos 1 / 2

0 (1 )

1 (1 )

( )

α α

π α α

α

α

co co

co co

co

co

rrc

f f f f f f

f f

f f

H f

, 2 2 ( ) ∑

∞

=−∞ ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ = − + +

k

pulse k k pulse

DP pulse

T d T

u t p t kT β

, otherwise

0

0

1/

( )

⎩

⎨

⎧ < < = max max q

β β β β
33

The pulse-shaping filter has a Gaussian frequency response 

where 

and B is the 3-dB bandwidth. 

A.3. Multi-Band OFDM Ultrawideband 

A block diagram of the simulated multi-band OFDM (MB-OFDM) transmitter is shown in 

Figure A-3. 

Figure A-3. Simulated MB-OFDM transmitter block diagram. 

Mathematical description of the MB-OFDM signal begins with defining the 128-tone coefficient 

vector 

( ) exp( ) , 2 2 P f f DP = −σ

2 2

ln 2

B

σ =

⎪

⎪

⎪

⎪

⎪

⎩

⎪

⎪

⎪

⎪

⎪

⎨

⎧

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛ +

= ⎟⎟

⎠

⎞ ⎜

⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛ +

= ⎟⎟

⎠

⎞ ⎜

⎜

⎝

⎛ ⎟

⎠

⎞ ⎜

⎝

⎛ +

=

=

=

otherwise data

4

1

2 exp

{6,16, 26,36, 46,56},{74,84,94,104,114,124} pilot 4

1

exp

{58,59,60,61,62},{68,69,70,71,72} modulated guard 4

1

exp

0 {63,64}, {65,66,67} zero guard

0 {1} center

n

n

n

n

a

j

j a n

j a n

n

n

D

π

π

π
34

where an is the n

th coefficient value that modulates data, pilot, and modulated guard tones. These 

coefficients take on values of {0, 1, 2, 3}, {0, 1}, and {0, 1} for data, pilot, and modulated guard 

tones respectively. Indices for the tone coefficient vector can be translated to actual frequencies 

by the equation 

where fc is the center frequency and ∆f is the 4.125-MHz tone spacing. 

Random coefficients QPSK modulate the data tones. A fixed pattern of coefficients {0, 1, 0, 0, 1, 

0, 0, 1, 0, 0, 1, 0} randomized by a bit from a 127-bit pseudorandom sequence, BPSK modulates 

the pilot tones. The same 127-bit pseudorandom sequence BPSK modulates the modulated guard 

tones between symbols. Guard tones within the same symbol are differentiated by a distinct 

offset into the 127-bit pseudorandom sequence. 

The tone coefficients are inverse Fourier transformed to data block samples by 

where Dn is the n

th QPSK symbol and N = 128. The MB-OFDM symbol (see Figure 6) is formed 

by placing the data block between the 32-sample zero prefix and 5-sample guard interval suffix 

making the total symbol length, L, 165 samples in duration. The k

th MB-OFDM symbol is 

The simulated MB-OFDM signal is mathematically described as 

where hrect is a filter with a rectangular frequency response bounded by ±1/(2∆t), TOFDM = L∆t = 

312.5 nanoseconds is the MB-OFDM symbol period, ∆t = 1/(N∆f) ≈ 1.9 nanoseconds, 

andR denotes the real part of a complex number. 

 

Finally, MB-OFDM signals hop between 14 band center frequencies according to the specified 

time-frequency code. The effect the time-frequency code has on a single frequency is simulated 

by multiplying the signal by the gate function g(t; T = bdTOFDM, τ = dTOFDM), where b is the 

number of bands, d is the number of periods the signal dwells in each band. The periodic gating 

function shown in Figure A-4 is defined mathematically as 

, 65 128

1 64

( 129)

( 1)

≤ ≤

≤ ≤

⎩

⎨

⎧

+ − ∆

+ − ∆ = n

n

f n f

f n f f

c

c

0,1, 2, ..., 1 , 2exp

1 1

0

⎟ = − ⎠

⎞ ⎜

⎝

⎛ = ∑

−

=

m N

N

j mn D

N

d

N

n

m n

π

( ) ( )( ) ( ) 1 exp 2 , 1

, ⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ ⎟ − ⎠

⎞ ⎜

⎝

⎛ = R ∑ ∑ − − − ∆

∞

= = −∞

u t u h t kT l t j f t OFDM c

k

L

l

k l rect π

.

0 160 165

32 160

0 0 32

, 33 ⎪

⎩

⎪

⎨

⎧

< ≤

< ≤

< ≤

= −

l

d l

l

uk l l
35

Figure A-4. Periodic gating function. 

A.4. Gated Gaussian Noise 

A block diagram of the simulated gated Gaussian-noise source is shown in Figure A-5. 

Figure A-5. Simulated gated Gaussian noise transmitter block diagram. 

Gated Gaussian noise is described by 

where ∗ is the convolutional operator and n(t) is a zero-mean Gaussian random process with the 

probability density 

where σn is the standard deviation of n(t). 

u(t;T,τ ) (n(t) h (t))g(t;T,τ ) , = ∗ BPF

, 2 exp 2

1 ( ) 2

2

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ − =

n n

n

q n

πσ σ

and . 0 otherwise

1 0

( ; , ) ( ; ) where ( ; ) T τ

t

g t T R t kT R t

k

>

⎩

⎨

⎧ ≤ < = ∑ − =

∞

=−∞

τ

τ τ τ
36

APPENDIX B: VECTOR SIGNAL GENERATOR OPERATION

The dual arbitrary waveform generator (AWG) inside the Agilent E8267C Vector Signal 

Generator (VSG) is used in this study to generate various types of interference signals. Complexbaseband (CBB) waveforms are simulated in software, loaded into VSG memory, and generated 

by the dual AWG. This appendix describes instrument operation, techniques to optimize RF 

performance, relevance of these techniques to our study, conditioning of simulated waveforms, 

and VSG operating procedures. 

B.1. Operation in the Dual AWG Mode 

A block diagram of the VSG operating in the dual AWG mode is shown in Figure B-1. Digital 

in-phase (I) and quadrature-phase (Q) signals, previously loaded into memory, are converted to 

analog I and Q signals by digital-to-analog converters (DACs) with digital interpolation filters 

and analog reconstruction (lowpass) filters. Interpolating filters increase the data rate by a factor 

of four and ease requirements of the reconstruction filters. Reconstruction filters smooth the 

stair-step responses that are present in the analog I and Q signals at the output of the DACs. 

Figure B-1. Block diagram of the Vector Signal Generator. 
37

The smoothed analog I signal at the output of the reconstruction filter modulates an LO while the 

corresponding Q signal modulates a 90° phase-shifted LO. These modulated signals are summed 

to produce a real bandpass signal centered at the LO frequency. After this, only the amplitude of 

the bandpass signal is adjusted. 

Signal amplitude is dependent upon the settings for the automatic level control (ALC) and the 

RF output power. When the ALC is on, the ALC circuitry senses the RF output power and uses 

this level in a feedback loop to maintain the RF output power at a constant calibrated level. The 

ALC causes unwanted amplitude fluctuations in signals with variable amplitudes. Since the 

interference signals have amplitude variations, we chose to turn off the ALC. 

B.2. Techniques to Optimize RF Output Performance 

Hardware I/Q modulation within the VSG generates undesirable signals as well as the desired 

waveform. Undesirable signals include local oscillator (LO) feed-through, intermodulation 

products, and other spurious responses. LO feed-through, produced by I and Q DC offset 

voltages within the I/Q modulator, is perhaps the most prominent unwanted signal. While 

generation of the undesirable signals is unavoidable, the amplitude of the undesirable signals can 

be minimized using various techniques including I/Q calibration, pre-distortion, RF blanking, 

and frequency shifting. 

B.2.1. I/Q Calibration and Adjustments 

Effects of imperfect I/Q modulation are compensated for by adjustments to parameters that 

include I and Q DC offsets, I/Q gain balance, I/Q timing skew, and quadrature skew. I and Q DC 

offset adjustments vary the DC voltage of I and Q signals, respectively. I/Q gain balance 

adjustment varies the gain of the I signal relative to the gain of the Q signal. I/Q skew adjustment 

delays I relative to Q or vice versa. Quadrature skew changes the phase angle between I and Q 

signals. 

I/Q adjustments can be made with custom or vendor-supplied internal I/Q adjustment routines. 

Custom routines consist of playing a test waveform through the VSG and monitoring its output 

on a spectrum analyzer (SA). Optimum I/Q modulation settings are found by repetitively 

adjusting the parameters and observing the effects of adjustment on the SA. For example, a twotone test signal, played through the VSG, can be used to minimize LO feed-through. Optimum 

DC offset settings are found by repetitive adjustment of these settings and monitoring the 

amplitude of the LO feed-through on the SA. In contrast, vendor-supplied routines use internal 

test waveforms and monitoring functions. 

Since both custom and vendor-supplied routines provided similar results, we chose the vendorsupplied routine to reduce development time and test-system complexity. To maintain the bestpossible performance, we perform I/Q calibration before each DTV interference susceptibility 

test. 
38

B.2.2. Waveform Pre-distortion 

Pre-distortion software is available from the manufacturer that can be used to further minimize 

the effects of I/Q modulator imperfections in addition to performing an I/Q calibration. This 

software requires that the VSG be connected to an Agilent PSA or ESA spectrum analyzer. The 

software then applies corrections to I and Q data to minimize undesirable signals as determined 

by automated measurements by the SA. 

While this technique can provide superb results in minimizing undesirable signals, it has several 

limitations that preclude its use in our measurements. First, this technique requires monitoring 

the RF output of the VSG on a SA any time data correction is desired. This would add more 

complexity to the measurement system. Second, changing the settings on the VSG requires new 

corrections to be made on I and Q waveform data. Perhaps an even more serious limitation is that 

this technique can only be used with waveforms that use a sampling rate of 100 million samples 

per second (MSps). Many of the waveforms that we use have other sampling rates. For these 

reasons, we chose not to use pre-distortion. 

B.2.3. RF Blanking 

RF blanking can be used to mitigate LO feed-through when the signal is known to be off. For our 

tests, this includes gating periods. RF blanking uses the pulse modulation circuitry in the VSG to 

turn off the RF output. The user generates a separate marker file that contains Boolean values 

corresponding to each data sample in the waveform file. Each Boolean value in the marker file 

instructs the pulse modulation circuitry to turn the RF output on or off while the corresponding 

data sample in the waveform is being played on the VSG. Using a marker file, the user can 

specify turning off the RF output whenever a waveform data sample is zero, thus squelching the 

LO feed-through that would normally be present in the RF output. The pulse-modulation 

circuitry used for RF blanking typically provides 80 dB of attenuation for the RF-off state. 

Practical limitations associated with RF blanking prevented us from using it. Most importantly, 

there is a finite rise and fall time associated with the pulse modulation circuitry. Unfortunately, 

the rise and fall times can be as long as 10 nanoseconds, which is as long as the sample period. 

Consequently, RF blanking can modulate the desired signal. This modulation is most evident 

when there are numerous transitions between zero and non-zero voltages. Additionally, the VSG 

introduces a delay between the time a waveform file sample is played and when RF blanking 

occurs. This delay is on the order of 40 to 50 nanoseconds and varies from one VSG to another. 

The delay must be experimentally determined and incorporated into the marker file. 

B.2.4. Frequency Shifting the Complex-Baseband Signal 

Another method of minimizing the effects of LO feed-through is to frequency shift the CBB 

signal by f0 during computer signal simulation so that the LO is outside the bandwidth of the 

desired signal as shown in Figure B-2. f0 should be chosen so that all significant spectral content 

of the desired signal is undistorted by the band edges of the VSG frequency response. If this 
39

distortion is unavoidable, it can be mitigated to some extent by applying an inverse filter, which 

compensates for the VSG frequency response, to the waveforms during simulation. As 

demonstrated in Appendix C, LO feed-through was not a problem for the DTV interference 

susceptibility tests; hence, we chose not to frequency shift. 

Figure B-2. Frequency shifting the complex-baseband signal by f0. 

B.3. Waveform Conditioning 

In this study, the simulated CBB waveforms of various interference signals are created and 

stored sequentially in a binary waveform file as a sequence of discrete-time, double-precision I 

and Q values. To be useful to the VSG, these values must be converted to a fixed-point format. 

This conversion process is performed in three steps: normalization, scaling, and quantization. 

Normalization is accomplished by finding the maximum of the absolute values of all I and Q 

data values and dividing all the I and Q values by this maximum value. After normalization, all I 

and Q values are within the range -1 ≤ {I, Q} ≤ 1. 

Scaling compensates for the effects of sample interpolation performed by the VSG prior to 

digital-to-analog conversion and reconstruction filtering. The interpolator, which runs at four 

times the AWG sample rate, often causes samples with large voltage transitions to exceed the 

range of the digital-to-analog converter (DAC). As recommended by the manufacturer and to 

ensure that DAC over-range errors do not occur, normalized I and Q data values are scaled by a 

factor of 0.7. After scaling, all I and Q values are within the range -0.7 ≤ {I, Q} ≤ 0.7. A less 

conservative scaling factor can be determined experimentally (on a signal by signal basis) by 

monitoring VSG DAC over-range error indicators. 

Finally, I and Q floating point values are converted to fixed-point numbers corresponding to the 

DAC resolution. For a 16-bit DAC, normalized and scaled floating-point values are multiplied 

by (215 – 1) and converted to 16-bit signed integer I and Q values. After quantization, all I and Q 

values are within the range -22,937 ≤ {I, Q} ≤ 22,937. Prior to loading the waveform into VSG 

RAM, it is important to resolve discrepancies between fixed-point formats that may exist 

between the computer, which processed the samples, and the VSG, which uses them. In our case, 
40

samples are processed on a PC using an Intel processor and played on a VSG using a Motorola 

processor. This requires a conversion from little endian fixed-point format to big endian. 

B.4. Operating Procedures 

In this study, VSG operational procedures are controlled via software using SCPI commands. 

This permits highly repeatable operation. The sequence of commands used to setup and operate 

the VSG is provided below: 

1. System Preset: Perform the same operation as the front panel Preset button. Set the VSG 

to the factory defined state. 

2. Clear Status Register: Clear the status byte register including the error queue and all 

event registers. 

3. ALC Off: Turn off automatic level control. 

4. Set RF Frequency: Set LO frequency that the I and Q samples modulate. 

5. Clear Waveform Memory: Delete WFM1 waveform memory (RAM) and wait 2 seconds 

before issuing the next command to allow deletion to complete. 

6. Load Waveform File: Load normalized waveform file from personal computer into 

WFM1 waveform memory using FTP. 

7. Select Waveform Segment: Select the waveform segment corresponding to file loaded 

into WFM1 waveform memory. 

8. Set Sample Rate: Set sample rate according to that specified in simulation. 

9. AWG On: Turn on baseband AWG and wait 10 seconds before issuing the next 

command to allow baseband reconfiguration. 

10. Modulation On: Enable waveform modulation of LO. 

11. Set Run-Time Scaling to 100%: The factory default is 70% and is only appropriate when 

loaded waveforms are not already properly normalized. 

12. I/Q Calibration: Perform I/Q calibration. 

13. Set RF Output Power Level. 

14. RF On: Turn on RF output. 
41

APPENDIX C: EFFECTS OF VSG LO FEED-THROUGH ON DTV INTERFERENCE 

SUSCEPTIBILITY TESTS 

This appendix provides a quantitative analysis of the effects of VSG LO feed-through on digital 

television (DTV) interference susceptibility tests. 

C.1. LO Feed-Through Inherent to DTV Systems 

As described in ETSI Measurement Guidelines for DVB Systems [19] and shown in Figure A-1, 

unwanted LO feed-through is inherent to quadrature-amplitude modulated (QAM) DTV systems. 

Note that QPSK receivers being tested in this effort are a subset of QAM. LO feed-through is 

often characterized by a carrier suppression specification defined as 

(C-1) 

where PDTV is the total power of the QAM signal without LO feed-through and PLO is the power 

of the LO feed-through. 

The signal has a noise equivalent bandwidth of BDTV, which we assume is equal to the QPSK 

symbol rate, RS. We also assume that the spectrum analyzer (SA) uses Gaussian-shaped filters 

whose noise equivalent bandwidth is approximately the resolution bandwidth (RBW). Since the 

noise and the DTV signal are zero-mean random processes, the mean powers of each signal can 

be separated arithmetically. As a result, the noise, signal, and LO feed-through power as well as 

the carrier suppression ratio can be computed from SA measurements. 

Various terms in the computation are used. PSD( f ) refers to power spectral density in watts/Hz, 

which is, in general, frequency dependent. P refers to power in watts. Finally, M refers to a SA 

power measurement in watts. For this appendix, M is measured in 1-kHz RBW with root-meansquare (rms) detection and 0.3-second integration time (i.e., sweep time divided by number of 

frequency steps). Subscripts nSYS, DTV, and LO refer to contributions from system noise, 

satellite DTV signal, and local-oscillator feed-through respectively. 

MnSYS is average power of the system noise measured with the system signal path cascaded with 

the SA, as shown in Figure 20, but with the DTV signal turned off. Noise from the SA and LNB 

are included in the measurement and we assume it to be flat across the satellite channel. From 

this measurement, power density of the system noise is computed as 

MDTV+nSYS is the average power of the DTV signal plus system noise measured by the SA at 

frequency f1 slightly offset from the center frequency of the DTV signal but still in the region 

,

LO

DTV

P

P CS =

( ) . RBW

M PSD f nSYS

nSYS =
42

where the DTV power spectral density is constant. The corresponding power spectral density 

without system noise is 

Average power of the DTV signal is 

MLO+DTV+nSYS is the average power of the LO feed-through plus DTV signal plus system noise 

measured by the SA centered on the DTV signal. From this measurement, power of the LO feedthrough is computed as 

Carrier suppression is computed from PLO and PDTV with equation C-1. 

Figure C-1 shows LO feed-through generated by the satellite simulation subsystem, described in 

Section 4.4, and measured by the spectrum analyzer. Satellite simulator power was set so that 

SNR was approximately 8.5 dB – the lowest SNR tested and therefore the most susceptible to LO 

feed-through from the VSG. The following measurements were made: MnSYS ≈ -90.5 dBm, 

MDTV+nSYS ≈ -82.0 dBm, MLO+DTV+nSYS ≈ -71.6 dBm. Calculations from these measurements give 

PLO ≈ -72.2 dBm, PDTV ≈ -38.6 dBm, and CS ≈ 34 dB. Note that these results are given in dB, 

but the calculations were performed in linear units. 

Figure C-1. LO feed-through from satellite signal generator. 

( ) ( ) . 1 PSD f RBW

M PSD f nSYS

DTV nSYS

DTV = − +

( ) ( ) . DTV DTV 1 DTV DTV 1 RS P = PSD f × B = PSD f ×

. PLO = M LO+DTV +nSYS − M DTV +nSYS
43

C.2. Experiment 

Satellite receivers that demodulate QPSK signals are designed to tolerate reasonable levels of LO 

feed-through. However, it is not clear how much additional LO feed-through, introduced by the 

VSG-generated interference signals in our DTV interference susceptibility test, can be tolerated. 

Figure C-2 depicts hardware used to emulate the VSG as it is used during DTV interference 

susceptibility testing to generate gated-noise signals from simulated waveforms. The noise diode, 

amplifier, and high-speed switch generate a gated-noise signal, while the signal generator adds a 

CW signal that emulates the VSG LO feed-through. 

Figure C-2. Interference subsystem for testing the effects of LO feed-through. 

The measurement procedure consisted of the following four steps: 

1. Baseline signal-quality measurement: The satellite simulation subsystem was set to 8.5-

dB SNR. Baseline interference, i.e., hardware-generated gated noise, was coupled into the 

test system at directional coupler DCPL-1 (see Figure 20) at a level that caused the 

segment error rate (SER) to be approximately 10-4. Baseline signal quality was recorded. 

2. Baseline-plus-CW signal-quality measurement: A CW signal was introduced at 

directional coupler DCPL-2 and signal quality was recorded with the DTV receiver 

exposed to baseline-plus-CW interference. The CW signal was centered in the DTV 

channel at 3820 MHz and increased in 5-dB steps. The baseline-plus-CW measurements 

were stopped at a CW level that caused SER to exceed 10-2. 

3. Baseline and baseline-plus-CW power measurement: The CW amplitude was set to the 

highest level that did not cause signal quality to deviate from the baseline signal quality. 

SA measurements at the CW frequency and offset from the CW frequency were made at 

the output of the LNB in 1-kHz RBW. Calculations using these measurements determined 

the amount of CW power alone. Figure C-3(a) shows SA measurements of hardwaregenerated baseline-plus-CW Gaussian noise interference (GN-01). 
44

4. VSG interference and LO feed-through power measurement: The hardware-generated 

interference subsystem was replaced by the VSG loaded with the same corresponding 

gated-noise waveform. VSG amplitude was adjusted so that the gated-noise power, away 

from the LO feed-through, matched the baseline power measured previously. SA 

measurements at the LO frequency and offset from the LO frequency were made at the 

output of the LNB in 1-kHz RBW. Calculations using these measurements were used to 

determine the amount of LO feed-through power alone. Figure C-3(b) shows SA 

measurements of VSG-generated Gaussian noise interference (GN-01). 

(a) (b) 

Figure C-3. SA measurements of (a) hardware-generated Gaussian-noise plus CW and (b) 

VSG-generated Gaussian noise with LO feed-through. 

C.3. Results 

Interference permutations considered for this experiment were continuous Gaussian noise (GN01), gated noise with 100-nanosecond on time and 50% duty cycle (GN-07), and gated noise 

with 100-nanosecond on time and 6.25% duty cycle (GN-09). VSG-generated GN-09 has the 

strongest LO feed-through relative to the intended-interference power. This is because gating 

attenuates the noise average power but has no effect on the LO feed-through power. In fact, GN09 LO feed-through will be the strongest encountered out of all gated-noise interference 

permutations listed in Section 2.1.5 because the VSG amplitude has to be set higher to create an 

SER = 10-4 condition. 

Figures C-4 – C-6 display best estimates of four DTV signal quality metrics, i.e., segment error 

rate (SER), pre-Viterbi bit error rate (BER), modulation error rate (MER), and error vector 

magnitude (EVM). These metrics are plotted against increasing levels of CW power representing 

VSG LO feed-through. Systematic error, introduced by the measurement system, has been 

mostly removed from these results. LO feed-through of the satellite simulator (black) and VSG 

(red) are plotted as vertical lines. Signal quality metrics began to degrade at CW powers of -60 

dBm, -62 dBm, and -65 dBm for GN-01, GN-07, and GN-09 respectively. 
45

Figure C-4. LO feed-through measurements for Gaussian noise interference. 

Figure C-5 LO feed-through measurements for gated noise interference with a 100-

nanosecond on-time and 25% duty cycle. 
46

Figure C-6. LO feed-through measurements for gated noise interference with a 100-

nanosecond on-time and 6.25% duty cycle. 

C.4. Effect of VSG LO Feed-Through on Carrier-Suppression Ratio 

The vertical red and black lines in Figure C-4 – C-6 show that LO feed-through of the satellite 

simulator and VSG were lower than the threshold where DTV quality metrics began to degrade. 

However, it is the combination of these residual carriers that we are concerned with. Figure C-7 

shows how the combination of LO feed-through powers degrade CS from 34 dB. 

The highest VSG LO feed-through for the gated-noise signals listed in Section 2.1.5 is 

approximately -74 dBm = -77 + 3 dBm, where -77 dBm is the LO feed-through power of the 

VSG generating GN-09 at a level that causes SER to be 10-4 (illustrated in Figure C-6) and 3 dB 

is a conservative estimate of the additional interference power needed to bring that SER to 10-1. 

Figure C-7 shows that this degrades carrier suppression to approximately 32 dB; this is the 

minimum CS for the gated-noise interference potential study. In other words, all gated-noise 

interference signals generated in this study will have a CS ≥ 32 dB. 

Results in Section C.3 show that, in the worst-case, DTV signal quality metrics began to degrade 

at an LO feed-through level of -65 dBm, which degraded carrier suppression to approximately 26 

dB. Therefore, as shown in Figure C-7 there is a 6-dB margin between the measured CS

threshold and the minimum CS for the gated-noise interference potential study. 
47

Figure C-7. Effects of VSG LO on carrier suppression. 

C.5. Conclusion 

This experiment shows that the highest VSG LO feed-through power, -74 dBm, for all gatednoise interference signals generated in this study degrades the carrier suppression ratio from 34 

to 32 dB. This is approximately 6 dB above the carrier suppression ratio that degraded signal 

quality metrics for a DTV receiver operating at SER ≈ 10-4. Hence, VSG LO feed-through is not 

a problem for our DTV interference susceptibility tests. 
48

APPENDIX D: ESTIMATING AND GRAPHING THE AMPLITUDE PROBABILITY 

DISTRIBUTION OF COMPLEX-BASEBAND SIGNALS 

The amplitude probability distribution (APD) function has been found to be useful in 

characterizing signals and evaluating effects of interference on victim receivers. The following 

discussion defines the APD mathematically and demonstrates how it can be estimated and 

plotted with the MATLAB® language. 

D.1. Estimating the APD

The mathematics and terminology of the APD are based on the concept of random variables. A 

random variable is a function, X, which assigns a real number, X(v), to every element, v, of a 

sample space. In our case, the random variable assigns a real number representing the amplitude 

of a complex-baseband signal drawn from a sample space via signal simulation or measurement. 

The APD is expressed as 

or more commonly 

where a is an amplitude value and ) ℘( means probability. 

A discrete estimate of the APD can be obtained from a finite set of samples. This is 

accomplished by sampling the complex-baseband signal N times, converting these samples to the 

amplitudes 

ordering the amplitudes from smallest to largest 

where the square brackets distinguish the ordered amplitudes from the unordered amplitudes, and 

computing 

The routine “[a, p] = apd(s)” (given in Appendix D.5) estimates the APD from N amplitude 

samples. Elements in the vector “s” are checked to make sure they are amplitudes, i.e., that they 

are real and positive. “s” is ordered from smallest to largest via “a = sort(s)”, where “sort” is a 

MATLAB function, and corresponding probabilities are computed via “p = 1-(1 : N)/N”. 

F(a) =℘(X (v) > a)

F(a) =℘(X > a) ,

a(n) , n = 1, 2,L, N ,

a[n] , n = 1, 2,L, N ,

( ) [ ] 1 . N

n

℘ X > a n = −
49

D.2. Plotting the APD

APDs are typically displayed on a Rayleigh graph whose axes are transformed by functions that 

linearize the APD of the Rayleigh-distributed amplitudes of complex Gaussian noise. Derivation 

of these functions begins with the expression of the APD of a Rayleigh-distributed random 

variable 

where σ represents the standard deviation of zero-mean complex noise. This function is 

linearized by the natural logarithm function and converted to decibels by 

Graphical coordinates, x and y, are introduced by 

 and 

where P0 is the probability at the graph origin. 

The x-axis is in units of percentage and labeled “percentage of time ordinate is exceeded” or 

“percent exceeding ordinate.” The x-axis probabilities generally range from 0.0001 to 99 percent 

for problems associated with modern receiver design. 

Signals are often normalized. Common normalization factors are the root-mean-square (rms) 

voltage of the signal or the rms voltage of white Gaussian noise. If normalized by the rms 

voltage of the signal, the y-axis is labeled “dB relative to rms voltage” or “dB relative to average 

power.” If normalized by the rms voltage of white Gaussian noise, the y-axis is labeled “dB 

relative to measurement system average noise power” or “dB relative to kTB.” Otherwise, units 

appropriate to the samples such as dBV or dBW are used. 

The routine “plotapd(a,p)” plots the APD on a Rayleigh graph. The routine begins by checking 

for zero amplitudes and probabilities that will generate log function error messages. Zero 

amplitudes are possible for signals simulated without added noise. The largest or peak amplitude 

of the APD estimate is assigned zero probability. “Plotapd” circumvents these problems by 

replacing the zero value with the MATLAB constant “not a number” (NaN). The MATLAB 

“log” or “log10” functions will not generate error messages when operating on NaNs and the 

MATLAB “plot” routine will not plot points in which the abscissa or ordinate is NaN. The 

remainder of the routine transforms data and axes to the graphical coordinate system using the 

equations for x and y above. y-axis labeling is dependent on how or if the samples were 

normalized and therefore is left outside the plotting routine. 

( ) exp , 2

2

⎟

⎟

⎠

⎞

⎜

⎜

⎝

⎛ − ℘ > = σ

a X a

10 log( ) 10log( ) 10log( ln( ( ))) . 2 2a = σ + − ℘ X > a

( ) 2

y = log a

10 log( ) ln( ) 10 log[ ln( ( ))] , x = − P0 − − ℘ X > a
50

D.3. Testing the Routines 

The routine “testapd” demonstrates the use of the “apd” and “plotapd” routines by checking the 

median and mean values of two test signals (corresponding plots are shown in Figures D-1 and 

D-2). The first test signal is a ten-thousand element vector of linearly increasing voltages ranging 

from 0 to 9999. The median (i.e., the amplitude exceeded by 50 percent of the samples) of this 

signal is 5000 V or 74.0 dBV. The second test signal is a one-million element vector of complex 

Gaussian noise voltages with zero mean and a variance of 2 V2

. The rms voltage is equal to the 

square root of the variance or 3.0 dBV. The corresponding probability is equal to 1/e or 0.367 

and expressed on the graph as 36.7%. 

D.4. Additional Comments 

Several important amplitude statistics, including rms voltage or average power, can be computed 

with the information found in the APD. However, the APD cannot provide information 

concerning the signal time-domain behavior without other a priori information such as knowing 

that it is a periodically pulsed signal. 

Additionally, the APD is strongly affected by the type of band-limiting filter; the APD estimate 

accuracy and precision are dependent on the sample interval and number of samples used. 

Consequently, these parameters should be documented with the APD graph. 
51

Figure D-1. APD of a ramp function increasing from 0 to 9999 V. 

Figure D-2. APD of complex Gaussian noise with mean = 0 V and variance = 2 V2

. 
52

D.5. MATLAB Code 

D.5.1. testAPD.m 

% testAPD 

% demonstrates how to use apd(s) and plotapd(a,p). 

% ramp 

N = 10.0^4; 

v = [0:N-1]; 

[a,p] = apd(abs(v)); 

plotapd(a,p); 

title(sprintf('Simulated ramp, 0-9999 V, N = 10^4, Peak %f dBV',20*log10(a(N)))) 

ylabel('dBV') 

pause 

%complex Gaussian noise 

N = 10.0^6; 

v = randn(1,N) + i*randn(1,N); 

[a,p] = apd(abs(v)); 

plotapd(a,p); 

title(sprintf('Simulated complex Gaussian noise, variance 2 V^2, N = 10^6, Peak %f 

V',20*log10(a(N)))) 

ylabel('dBV') 

D.5.2. apd.m 

function [a, p] = apd(s); 

% [a, p] = apd(s) estimates the amplitude probability distribution function. 

% 

% input parameters: 

% s = amplitude samples 

% 

% return variables: 

% a = ordered amplitudes 

% p = probability that the ordered ampitude is exceeded 

if isreal(s) & min(s)>=0 

 a = sort(s); 

 N = length(a); 

 p = 1 - [1:N]/N; 

else 

 disp('Input values must be amplitudes i.e. real and positive values.'); 

end 
53

D.5.3. plotapd.m 

function plotapd(a,p) 

% plots (a,p) pairs representing the APD on Rayleigh graph. 

% 

% input variables: 

% a = ordered amplitudes 

% p = probability of ordered amplitude 

% x-axis labels (labels must be same length) 

xticklabel = ['0.0001';' 0.01';' 0.1';' 1';' 5';' 10';' 20';' 30';' 40';' 50';' 60';' 70';' 

80';' 90';' 95';' 98';' 99']; 

ptick = [0.0001 .01 0.1 1 5 10 20 30 40 50 60 70 80 90 95 98 99]/100; 

porigin = ptick(1); 

% replace 0 valued amplitudes with NaN to avoid logarithm error messages 

if min(a) == 0 

 idx = find(a==0); 

 a(idx) = NaN; 

end 

% replace peak amplitude 0 probability with NaN to avoid logarithmic error messages 

p(length(a)) = NaN; 

% map a and p and plot 

x = 10.0*log10(-log(porigin)) - 10.0*log10(-log(p)); 

y = 20.0*log10(a); 

plot(x,y,'LineWidth',2); 

grid 

% customize x axis 

xlabel('percent exceeding ordinate'); 

xtick = 10.0*log10(-log(porigin))-10.0*log10(-log(ptick)); 

xLim([min(xtick) max(xtick)]); 

set(gca,'XTick',xtick); 

set(gca,'XTickLabel',xticklabel); 

return 
54

APPENDIX E: DYNAMIC RANGE OF THE VECTOR SIGNAL ANALYZER 

Statistics such as average power, amplitude probability distribution (APD), and power spectral 

density are measured with the vector signal analyzer (VSA) in this study. Accurate estimates of 

these statistics require the amplitudes of measured signals to lie within the dynamic range of the 

VSA. This appendix describes how average powers of measured signals are set to ensure 

accurate VSA measurements. First, a continuous wave (CW) dynamic range measurement is 

described. Next, average power measurements of four fundamental signals with differing peakto-average power ratios (P/A) are described, and APD measurements of these signals are 

provided. Finally, guidelines for setting the average power of measured signals are given. 

Four fundamental signals are considered in this appendix: CW, Gaussian noise, power-Rayleigh 

noise [20] with an exponent of 4, and a periodic pulse train with a 1-MHz pulse repetition 

frequency (PRF). All signals, including CW, were simulated in complex baseband (CBB) at a 

sample rate of 100 million samples per second (MSps), loaded into the VSG memory, 

upconverted to 3820 MHz, and applied to the input of the VSA. A power meter was used to 

verify average signal power applied to the input of the VSA. The VSA center frequency and 

input range were set to 3820 MHz and -30 dBm, respectively, and measurements were made in 

36- and 3-MHz VSA spans. 

E.1. Dynamic Range Measurement 

Dynamic range was measured by applying incrementally stronger CW signals to the VSA and 

recording amplitude estimates. The CW signal was generated with the vector signal generator 

(VSG) and connected to the VSA with a coaxial cable. VSA measurements were performed at 

increasing power levels in 2-dB steps. The initial input level was well below the noise floor of 

the VSA and the highest amplitude level was above its 1-dB compression point. Power levels 

where VSA overload occurred were noted. 

Blue curves in Figures E-1 and E-2 show results from the dynamic range measurements. 

Average power computed from VSA samples is reported as “measured power” and average 

power measured by the power meter at the VSA input is reported as “input power.” VSA 

overdrive occurred at the same input power for both spans. The noise floor in the 36-MHz span, 

i.e., -77 dBm, is approximately 11 dB higher than the noise floor in the 3-MHz span, i.e. -88 

dBm, which corresponds to the difference in bandwidths. 

For the purposes of this study, dynamic range is defined as the straight portion of the CW curves 

in Figures E-1 and E-2. It is approximately 50 dB and 60 dB for the 36- and 3-MHz VSA spans. 

E.2. Average Power Measurements of Fundamental Signals 

The dynamic range measurement procedure was repeated for Gaussian noise, power-Rayleigh 

noise, and a periodic pulse train. Results are plotted in Figures E-1 and E-2; the periodic pulse 

train is denoted by the acronym UPS, which stands for uniform pulse spacing. As shown in Table 
55

E-1, VSA overload occurred at the lowest average power for power-Rayleigh noise, next for the 

periodic pulse train, and last for Gaussian noise. All occurred well below the CW overload. 

Interestingly, the measured power departed from the straight, top-portion of the CW curve at 

varying input powers. All measured powers departed from the straight, bottom-portion of the 

curve at the same input power, i.e., approximately -70 dBm and -80 dBm for the 36- and 3-MHz 

VSA spans, respectively. 

Table E-1. Minimum Input Power of Signals Causing VSA Overload (dBm) 

VSA Span 36 MHz 3 MHz 

CW -20 -20 

Gaussian Noise -32 -42 

Power-Rayleigh -48 -57 

1-MHz Pulse Train -32 -43 

E.3. APD Measurements of Fundamental Signals 

Next, APDs were calculated from the VSA measurements performed previously and plotted in 

Figures E-3 through E-10. These plots show how the choice of average power affects APD

measurement accuracy, which is made evident by accurate replication of APD features. For 

example, peak features were poorly replicated when strong amplitudes caused amplifier 

compression. Table E-2 shows the range of average powers where APDs were measured 

accurately. Each signal has a distinct P/A, defined as the power present 0.01% of the time 

divided by the average power. Gaussian noise P/A is approximately 10 dB, and CW P/A is 0 dB. 

Unlike Gaussian-noise and CW P/A, power-Rayleigh and periodic-pulse P/A are dependent on 

the measurement bandwidth. Table E-2 gives P/A measured in 36- and 3-MHz VSA spans. 

Table E-2. Information from APDs Measured by the VSA 

Input Power Range where APDs 

were Measured Accurately (dBm) P/A (dB) 

VSA Span 36 MHz 3 MHz 36 MHz 3 MHz 

CW -40/-20 -50/-20 0 0 

Gaussian Noise -63/-33 -73/-43 10 10 

Power-Rayleigh -63/-43 -84/-54 21 15 

1-MHz Pulse Train -53/-33 -64/-34 15 4 

E.4. Conclusion 

Results show that the average input power of signals measured by the VSA should be determined 

from careful consideration of VSA dynamic range and characteristics of the signal. Failure to do 

so will result in inaccurate average power and APD estimates. As a guideline for VSA signal 

characterization measurements, the average power of the measured signal should be set 20 - 25 

dB above the bottom of the VSA dynamic range. Using this guideline, average powers of 
56

measured signals were set to -45 dBm and -55 dBm for 36- and 3-MHz VSA spans, respectively. 

Signals with higher P/A than those used in this appendix can be accurately measured by reducing 

the average power accordingly. 

 

Figure E-1. Measured average power versus input average power of the VSA at span = 36 MHz. 

Figure E-2. Measured average power versus input average power of the VSA at span = 3 MHz. 
57

Figure E-3. APDs of continuous-wave signals at decreasing power levels. Average power at 

VSA input is shown at right edge. 

Figure E-4. APDs of continuous-wave signals at decreasing power levels. Average power at 

VSA input is shown at right edge. 
58

Figure E-5. APDs of Gaussian noise at decreasing power levels. Average power at VSA input 

in 36-MHz span is shown at right edge. 

Figure E-6. APDs of Gaussian noise at decreasing power levels. Average power at VSA input 

in 3-MHz span is shown at right edge. 
59

Figure E-7. APDs of power-Rayleigh signals at decreasing power levels. Average power at 

VSA input in 36-MHz span is shown at right edge. 

Figure E-8. APDs of power-Rayleigh signals at decreasing power levels. Average power at 

VSA input in 3-MHz span is shown at right edge. 
60

Figure E-9. APDs of 1-MHz PRF, uniform-pulse spaced pulses at decreasing power levels. 

Average power at VSA input in 36-MHz span is shown at right edge. 

 

Figure E-10. APDs of 1-MHz PRF, uniform-pulse spaced pulses at decreasing power levels. 

Average power at VSA input in 3-MHz span is shown at right edge. 
61

 APPENDIX F: TEST SYSTEM CHARACTERIZATION 

This appendix provides characterization measurements and analyses of various parts of the DTV 

interference susceptibility test system. 

F.1. Characterization of the Signal Paths 

The interference and satellite signal paths of the DTV interference susceptibility test system, 

shown in Figure 20, were characterized by gain, 1-dB compression point, noise figure, and 

frequency response. The interference signal path is defined from the interference signal input of 

directional coupler DCPL-1 to the input of the vector signal analyzer (VSA), and the satellite 

signal path is defined from the satellite signal input of DCPL-1 to the input of the VSA. 

Gain and 1-dB compression point were measured using a continuous-wave (CW) input signal at 

3820 MHz. The input compression point is expressed as an average power at the input of DCPL1. The output compression point is defined at the VSA input and expressed as an average power 

and corresponding peak voltage. Peak voltage, expressed as 

is commonly used by the VSA and can be measured with an oscilloscope at the VSA input. 

Noise figures of both the interference and satellite signal paths were measured using a noise 

diode and spectrum analyzer according to the Y-Factor Method as described in [21]. 

Table F-1 summarizes the results for both interference and satellite signal paths. Note that the 

measurements of the output 1-dB compression point for both signal paths are within 0.1 dB. 

Within the precision of the measurement, these values should be considered to be the same. 

Table F-1. System Parameters for the Victim Receiver and Calibration Subsystems 

 Noise 

Figure 

(dB) 

Gain 

(dB) 

Input 1-dB 

Compression 

Point (Average 

Power; dBm) 

Output 1-dB 

Compression 

Point (Average 

Power; dBm) 

Output 1-dB 

Compression 

Point (mVpeak) 

Interference 

Signal Path 18.7 34.9 -35.3 -1.4 269.2 

Satellite Signal 

Path 9.3 44.5 -44.8 -1.3 272.3 

Magnitude of the system frequency response was measured by injecting a CW input signal that is 

incrementally increased in frequency in discrete steps (from 3770 MHz to 3870 MHz) into 

directional coupler DCPL-1 and measuring the output (at the input to the VSA) with a spectrum 

analyzer (SA), which tracked the CW input signal in zero-span mode. Both the CW signal 

generator and the SA operated under computer control to perform this measurement. 

2 50 10 , ( −30) /10 = × × PdBm Vpeak
62

Measurements of both the interference and satellite signal paths are shown in Figures F-1 and F2. Figure F-1 shows system gain over a 100-MHz bandwidth, which is dominated by the 40-

MHz, 1-dB bandpass filter (BPF1 in Figure 20). Note that the interference signal path has 

approximately 10 dB less gain than the satellite signal path due to attenuation in the directional 

coupler. Figure F-2 shows system gain over the 40-MHz passband of BPF1 at higher resolution. 

Note that the flatness over the passband for the entire signal path is approximately ±0.75 dB. 

Figure F-1. Frequency response of signal paths. 

Figure F-2. Passband response of signal paths. 
63

F.2. Dynamic Range of Interference Signal Path Cascaded with the VSA 

During calibration measurements in the DTV interference susceptibility test, the VSA is used to 

measure interference signals at the output of the interference signal path. It is important to ensure 

that these measurements are performed at signal levels that do not compress the LNB or the 

VSA, but are high enough above the noise floor so that lower amplitudes are accurately 

measured. 

Dynamic range measurements, similar to those described in Section E.1, are displayed in Figure 

F-3. The input range of the VSA was selected to be -10 dBm. Dynamic range of the interference 

signal path cascaded with the VSA, defined by the straight portion of the CW curve, is 

approximately 34 dB. In the bandwidth of the VSA, peak-to-average ratios of interference 

signals do not exceed 25 dB. Setting the average power of these signals to 25 dB below the top 

of the dynamic range eliminated compression effects and provided a 9-dB margin to accurately 

measure low amplitudes within the signal. 

Figure F-3. Dynamic range of the interference signal path cascaded with the VSA. 

F.3. Intermodulation Products in Satellite Signal Generator 

As a part of the satellite signal simulator shown in Figure 17, the upconverter frequency shifts 

the satellite signal from the modulator intermediate frequency of 70 MHz to the satellite channel 

center frequency of 3820 MHz. Shifting is done in two “mixer” stages. In the first stage, the 

1260-MHz local oscillator (LO) frequency produces lower and upper sideband signals centered 

at 1190 and 1330 MHz. Bandpass filter BPF3 passes the upper sideband signal to the second 

stage whose 5150-MHz LO frequency produces lower and upper sidebands centered at 3820 and 

6480 MHz. Bandpass filter BPF1 in the coupling subsystem (see Figure 20) passes the lower 

sideband to the LNB. 
64

Practical mixer components, represented by mixer-1 and mixer-2 in Figure F-4, create higherorder intermodulation (IM) products along with the desired frequency conversion products. 

Figures F-5 and F-6 show the theoretical frequency and corresponding order of these products 

for the two mixing stages. The 70-MHz intermediate frequency (IF) signal at the input of mixer1 is depicted by the only first-order, black-horizontal line in Figure F-5. It is 36-MHz wide, 

which is large compared to the 19.51-MHz wide DTV signal actually being used for testing. 

Mixing the IF signal with a 1260-MHz LO (shown as a red circle) produces multiple mixing 

products depicted by the black-horizontal lines at IM orders greater than one. The desired 

frequency conversion product of the first stage is the upper-sideband signal centered at 1330 

MHz, which lies in the passband of bandpass filter BPF3 and is the IF for the second mixing 

stage (illustrated by the only first-order, black-horizontal line in Figure F-6). High-order mixing 

products of the second mixing stage are attenuated by BPF1 (shown in Figure 20). Notice that 

for both mixing stages there are no mixing products below tenth order that lie within the 36-MHz 

passband of the satellite signal path (bounded by the blue-vertical-dashed lines) – i.e. no higherorder mixing products overlap the desired frequency conversion products. Therefore, the 

simulated satellite signal centered at 3820 MHz is uncorrupted by higher-order IM products. 

Figure F-4. Upconverter block diagram. Hardware is given in Appendix G. 
65

Figure F-5. IM products due to the first mixing stage of the satellite signal generator. 

Figure F-6. IM products due to the second mixing stage of the satellite signal generator. 
66

APPENDIX G: HARDWARE SPECIFICATION 

G.1. Interference Generation Subsystem 

Amp (Figure C-2) = Amplifier 

 Manufacturer: Unknown 

 Model: 3742-3-35 

 Serial: 1108 

 Gain: 38 dB 

 Frequency Range: 3.7 – 4.2 GHz 

AWG (Figure C-2) = Arbitrary Waveform Generator 

 Manufacturer: Agilent 

 Model: 33250A 

 

BPF4 (Figure C-2) = Bandpass Filter 

 Manufacturer: Microwave Filter Co. 

 Model: 7892-SMA-A 

 Frequency Range: 3.7 – 4.2 GHz 

DCPL - 2 (Figure C-2) = Directional Coupler 

 Manufacturer: Norsal Ind. 

 Model: 4871-10 

 Frequency Range: 0.2 – 18 GHz 

Gating Switch (Figure C-2) 

 Manufacturer: Mini-Circuits 

 Model: ZASWA-2-50DR 

 Frequency Range: dc – 5 GHz 

 Switching Time: 5 ns (typical), 15 ns (maximum) 

Noise Diode (Figure C-2) 

 Manufacturer: Noise/Com, Inc. 

 Model: NC3208A 

 Serial: E804 

 Frequency Range: 0.1-18 GHz 

 ENR = 27 dB 

Signal Generator (Figure C-2) 

 Manufacturer: Agilent 

 Model: E8267C option 520 (VSG) 

 Frequency Range: 250 kHz – 20 GHz 

 Resolution: 0.001 Hz CW, 0.001 Hz all sweep modes 

 Switching Speed: < 12 ms typical 
67

Variable Attenuator (Figure C-2) 

 Manufacturer: Hewlett Packard 

 Model: HP 8494H and HP 8496B 

 Frequency Range: 0 – 18 GHz 

 Attenuation Range: 0 – 11 dB at 1-dB step and 0 – 110 dB at 10-dB step 

Variable Attenuator (Figure C-2) 

 Manufacturer: Narda Microwave 

 Model: 791 FM 

 Frequency Range: 2 – 12.4 GHz 

 Attenuation Range: 0 – 40 dB 

VSG (Figures 13, 20, and B-1) = Vector Signal Generator 

 Manufacturer: Agilent 

 Model: E8267C option 520 

 Frequency Range: 250 kHz – 20 GHz 

 Resolution: 0.001 Hz CW, 0.001 Hz all sweep modes 

 Switching Speed: < 12 ms typical 

G.2. Calibration Subsystem/Test and Measurement Equipment 

BPF2 (Figure 20) = Bandpass Filter (tunable) 

 Manufacturer: K&L Microwave 

 Model: 5BT-750/1500-5N/N 

 Serial: 82-9839 

 Frequency Range: 750 - 1500 MHz 

 Percentage Bandwidth: 5% 

Oscilloscope (Figure 16) – Digital Phosphor Oscilloscope 

 Manufacturer: Tektronix 

 Model: TDS7704B 

 Hardware Analog Bandwidth: 7 GHz 

 Rise Time: 62 ps 10% to 90% (typical) 

 Memory: 1 MByte (standard) 

Power Divider (Figure 20) 

 Manufacturer: Midwest Microwave 

 Model: PWD-5520-03-SMA-79 

 Frequency Range: 0.5 – 2.0 GHz 

 Isolation: 15 dB typical 

 VSWR: 1.5:1 
68

Power Meter (Figure 20) 

 Manufacturer: Agilent 

 Model: E4419B 

 Frequency Range: 9 kHz – 110 GHz, sensor dependent 

 Power Range: -70 to +44 dBm, sensor dependent 

Power Meter Sensor (Figure 20) 

 Manufacturer: Agilent 

 Model: E9300A 

 Frequency Range: 10 MHz – 18 GHz 

 Power Range: -60 to +20 dBm 

Spectrum Analyzer (Figures 15 and 20) 

 Manufacturer: Agilent 

 Model: E4440A 

 Frequency Range: DC – 26.5 MHz 

 Maximum Resolution Bandwidth: 8 MHz 

Vector Signal Analyzer (Figures 14 and 20) 

 Manufacturer: Agilent 

 Model: 89641A 

 Frequency Range: DC – 6 GHz 

 IF Sample Rate: 95 MS/s 

G.3. Satellite Simulation Subsystem 

1260-MHz LO (Figure F-4) = Signal Generator 

 Manufacturer: Hewlett-Packard 

 Model: HP83413 

 Frequency Range: 10 MHz – 20 GHz 

5150-MHz LO (Figure F-4) = Signal Generator 

 Manufacturer: Hewlett-Packard 

 Model: HP 8672A 

 Frequency Range: 2 GHz – 18 MHz 

A1 (Figure 20) – Attenuator 

Attenuator/switch driver (Figure 20) 

 Manufacturer: Agilent 

 Model: 11713A 
69

BPF3 (Figure F-4) = Bandpass Filter (tunable) 

 Manufacturer: K&L Microwave 

 Model: 5BT-750/1500-5N 

 Serial: 8913-1 

 Frequency Range: 750 – 1500 MHz 

 Percentage Bandwidth: 5% 

 

Digital Video Stream (Figure 19) – PCI-Bus Board for Uncompressed Audio/Video Recording 

 Manufacturer: DVS 

 Model: ClipStationPRO 

Mixer – 1 (Figure F-4) = Frequency Mixer 

 Manufacturer: Mini-Circuits 

 Model: ZFM-2000 

 LO/RF Frequency Range: 100 – 2000 MHz 

 IF Frequency Range: DC – 600 MHz 

 Maximum Conversion Loss: 9.5 dB 

 Minimum LO/RF Isolation: 20 dB 

 

Mixer – 2 (Figure F-4) = Frequency Mixer 

 Manufacturer: Mini-Circuits 

 Model: ZX05-C60 

 LO/RF Frequency Range: 1600 – 6000 MHz 

 IF Frequency Range: DC – 2000 MHz 

 Maximum Conversion Loss: 8.3 dB 

 Minimum LO/RF Isolation: 23 dB (typical), 17 dB (minimum) 

 

Modulator (Figure 19) 

 Manufacturer: ComTech EF Data 

 Model: SDM-2020 

MPEG-2 Encoder (Figure 19) 

 Manufacturer: Motorola 

 Model: SE-1000 

 Input: analog or digital 

 Output: DVB-ASI, DHEI, or DS3 

Switch – 1 (Figure 20) 

 Manufacturer: Dow-Key Microwave 

 Model: 401-2308 

 Frequency Range: 0 – 26.5 GHz 

 RF Circuit: SPDT 
70

VA (Figure 20) = Step Variable Attenuator 

 Manufacturer: Hewlett Packard 

 Model: HP 8494H and HP 8496B 

 Frequency Range: 0 – 18 GHz 

 Attenuation Range: 0 – 11 dB at 1-dB step and 0 – 110 dB at 10-dB step 

G.4. Coupling Subsystem 

BPF1 (Figure 20) = Bandpass Filter 

 Manufacturer: TTE 

 Model: 310-3820M-40M-A 

 Center Frequency: 3820 MHz 

 1-dB Bandwidth: 40 MHz 

 50-dB Bandwidth: 110 MHz 

 Insertion Loss: ≤ 4 dB 

 VSWR: 1.5:1 

DCPL – 1 (Figure 20) = Directional Coupler 

 Manufacturer: Narda. 

 Model: 4202B-10 

 Frequency Range: 1 – 12.4 GHz 

 Coupling: 10 dB 

 Insertion Loss: 1.3 dB (maximum) 

 Directivity: 15 dB for 1 – 8 GHz, 12 dB for 8 – 12 GHz 

 VSWR: 1.35 

LPF (Figure 20) = Low-pass Filter 

 Manufacturer: TTE 

 Model: LMC11-7G-A 

 3-dB Maximum Frequency: 7 GHz 

 50-dB Minimum Frequency: 13 – 20 GHz 

 Insertion Loss: ≤ 0.2 dB 

 VSWR: 1.5:1 
71

G.5. Receiver Subsystem 

Bias Tee (Figure 20) 

 Manufacturer: Orbital 

 Model: BT1–V2-F-S-B-B 

 Frequency Range: 900 – 2100 MHz 

 Insertion Loss: 0.5 dB 

 Passband Ripple: 0.3 dB (maximum) 

 VSWR: 1.3:1 

 LNB Interface: 75Ω F-type 

 Receiver Interface: 50Ω SMA 

LNB (Figure 20) – LNA plus Block Downconverter 

 Manufacturer: Norsat International, Inc. 

 Model: 3120 C-band PLL LNB 

 Input Frequency Range: 3.4 – 4.2 GHz 

 LO Freq: 5.15 GHz 

 Stability: +/- 5 kHz 

 Noise Temperature = 20 K 

 Gain: 60 dB 

MPEG-2 Stream Monitor (Figure 20) 

 Manufacturer: Tektronix 

 Model: MTM400 

Satellite Receiver (Figure 20) 

 Manufacturer: Motorola/General Instrument 

 Model: DSR4500 NTSC 

Switch - 2 (Figure 20) 

 Manufacturer: Dow-Key Microwave 

 Model: 401-2308 

 Frequency Range: 0 – 26.5 GHz 

 RF Circuit: SPDT 

ZM (Figure 20) = 50Ω /75Ω Impedance Matching Pad 

 Model: 9086-50/75 

 Power: 1 W 

 Frequency: 3 GHz 

 Maximum VSWR: 1.25:1 

 Insertion Loss: 5.7 dB 
72

 APPENDIX H: GLOSSARY

ADC analog-to-digital converter 

ALC automatic level control 

APD amplitude probability distribution 

AWG arbitrary waveform generator 

BER bit error rate (italicized BER denotes measured pre-Viterbi bit error rate)

BPF band-pass filter 

BPSK binary phase-shift keying 

CBB complex baseband 

CRADA cooperative research and development agreement 

CS carrier suppression 

CW continuous wave 

DAC digital-to-analog converter 

dB decibel 

dBm decibel referenced to 1 milliwatt 

dBV decibel referenced to 1 volt 

dBW decibel referenced to 1 watt 

DCPL directional coupler 

DO digital oscilloscope 

DP-UWB dithered-pulse ultrawideband 

DS-UWB direct-sequence ultrawideband 

DTV digital television 

DVB digital video broadcast 

ETSI European Telecommunications Standards Institute 

EVM error vector magnitude

FEC forward error correction 

FCC Federal Communications Commission 

FIR finite impulse response 

FTP file transfer protocol 

GHz gigahertz = 109

 cycles per second 

GN Gaussian noise 

GSps 109

 samples per second 

I in-phase 

IF intermediate frequency 

IEEE Institute of Electrical and Electronics Engineers, Inc. 

IM intermodulation 

IRD integrated receiver decoder 

ITS Institute for Telecommunication Sciences 

ITU International Telecommunication Union 

LNB low-noise block downconverter 

ln natural logarithm 

LO local oscillator 

log base-10 logarithm 

LPF low-pass filter 

LSB least significant byte 
73

MAC medium access control 

MB-OFDM multi-band orthogonal frequency-domain multiplex 

MBOA-SIG MB-OFDM Alliance Special Interest Group 

Mbps million bits per second 

MER modulation error rate

MHz megahertz = million cycles per second 

MPEG Motion Picture Experts Group 

MSB most significant byte 

MSps million samples per second 

ns nanosecond = 10-9 seconds 

NTIA National Telecommunications and Information Administration 

P/A peak-to-average ratio 

PHY physical layer 

PLCP physical layer convergence procedure/protocol 

PMD physical medium dependent 

PRF pulse repetition frequency 

PSD power spectral density 

Q quadrature phase 

QAM quadrature amplitude modulating 

QPSK quadrature phase-shift keying 

RBW resolution bandwidth 

RF radio frequency 

Rms root-mean-square 

RRC root-raised cosine 

RS Reed-Solomon 

SA spectrum analyzer 

SCPI Standard Commands for Programmable Instrumentation 

SER segment error rate 

SMPTE Society of Motion Picture and Television Engineers 

SNR signal-to-noise ratio 

TEF transport error flag 

UPS uniform pulse spacing

UTC coordinated universal time 

UWB ultrawideband 

V volt 

VA variable attenuator 

VBW video bandwidth 

VSA vector signal analyzer 

VSG vector signal generator 

WLAN wireless local area network 

WPAN wireless personal area network